An unfinished guide to Industry 4.0 in Industries. This can be used by anyone to teach Industry 4.0 anywhere. You can add material to it. And I will also build up the presentation with some more details. This can also be used in conjunction with other presentations like "How did Industry 4.0 Begin". Overall a comprehensive guide.
The document discusses the four industrial revolutions: Industry 1.0 focused on mechanization, Industry 2.0 added electrical power, Industry 3.0 brought digital technology, and Industry 4.0 integrates cyber-physical systems using IoT, cloud, and cognitive computing. Industry 4.0 enables technologies like augmented reality, big data analytics, autonomous robots, additive manufacturing, simulation, system integration, and cybersecurity. It aims for interconnected smart factories through technologies that enable interoperability, transparency, assistance, and decentralized decision making.
This document discusses how smart manufacturing and artificial intelligence of things (AIoT) can help drive digital transformation. It provides examples of how IoT solutions have helped various companies reduce costs and improve operations. It then discusses key concepts in smart manufacturing like the intelligent edge, cloud computing, and different waves of innovation with IoT, edge, and AI. The document outlines Microsoft's IoT portfolio and reference architecture for smart manufacturing. It also describes various Azure IoT capabilities and solutions like IoT Hub, IoT Edge, Time Series Insights, and preconfigured solutions for predictive maintenance, remote monitoring and connected factories. Finally, it discusses how machine learning can address supply chain optimization, predictive maintenance, anomaly detection, production scheduling and demand
We’re in the midst of a significant transformation regarding the way we produce products thanks to the digitization of manufacturing. This compelling transition is called Industry 4.0 – which is a representation of the fourth revolution that has occurred in manufacturing. Like the three industrial revolutions which preceded it – steam power, mass production/electricity, digital age – Industry 4.0 will transform local and global economies and create a new future for us all.
Industry 4.0 and Internet of Things (IoT)- The Emerging Marketing TrendsSuyati Technologies
The document discusses industry 4.0 and the emerging trends of the industrial internet of things (IIoT). It notes that IIoT is seen as a primary way to improve operational efficiency in manufacturing. By 2020, the number of connected devices is expected to reach 51 billion and IIoT is projected to add $14.2 trillion to the global economy. Key benefits of IIoT include improved productivity, reduced costs, enhanced safety, and new business models.
Impact for Educational Institutions, Internet of things, Digital Enablers, New Age Production, Smart Factory, New digital industrial technology, Interdisciplinary Thinking, Digital Work Place, 3d printing,
Industry 4.0 refers to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, IoT, cloud computing and cognitive computing. Key features include interconnectivity, increased customization through 3D printing, integration of advanced analytics, reliance on cloud computing and use of autonomous robots. Challenges to implementing Industry 4.0 include high costs, lack of standards, security issues, disruptions to business models and potential job losses. For Bangladesh's garment industry, challenges include lack of government support, infrastructure and knowledge as well as availability of cheaper labor. Industry 5.0 is emerging as a future trend focused on closer human-machine cooperation and waste prevention.
This document appears to be a presentation about cyber-physical systems and Industry 4.0. It includes definitions of cyber-physical systems and Industry 4.0, examples of companies implementing Industry 4.0 solutions, discussions of the Internet of Things, impacts of new technologies on the economy, business, society and individuals, and slides on various related topics without titles like applications, history, and use cases. The presentation touches on many aspects of emerging digital technologies and their integration with physical systems and processes.
The document discusses the four industrial revolutions: Industry 1.0 focused on mechanization, Industry 2.0 added electrical power, Industry 3.0 brought digital technology, and Industry 4.0 integrates cyber-physical systems using IoT, cloud, and cognitive computing. Industry 4.0 enables technologies like augmented reality, big data analytics, autonomous robots, additive manufacturing, simulation, system integration, and cybersecurity. It aims for interconnected smart factories through technologies that enable interoperability, transparency, assistance, and decentralized decision making.
This document discusses how smart manufacturing and artificial intelligence of things (AIoT) can help drive digital transformation. It provides examples of how IoT solutions have helped various companies reduce costs and improve operations. It then discusses key concepts in smart manufacturing like the intelligent edge, cloud computing, and different waves of innovation with IoT, edge, and AI. The document outlines Microsoft's IoT portfolio and reference architecture for smart manufacturing. It also describes various Azure IoT capabilities and solutions like IoT Hub, IoT Edge, Time Series Insights, and preconfigured solutions for predictive maintenance, remote monitoring and connected factories. Finally, it discusses how machine learning can address supply chain optimization, predictive maintenance, anomaly detection, production scheduling and demand
We’re in the midst of a significant transformation regarding the way we produce products thanks to the digitization of manufacturing. This compelling transition is called Industry 4.0 – which is a representation of the fourth revolution that has occurred in manufacturing. Like the three industrial revolutions which preceded it – steam power, mass production/electricity, digital age – Industry 4.0 will transform local and global economies and create a new future for us all.
Industry 4.0 and Internet of Things (IoT)- The Emerging Marketing TrendsSuyati Technologies
The document discusses industry 4.0 and the emerging trends of the industrial internet of things (IIoT). It notes that IIoT is seen as a primary way to improve operational efficiency in manufacturing. By 2020, the number of connected devices is expected to reach 51 billion and IIoT is projected to add $14.2 trillion to the global economy. Key benefits of IIoT include improved productivity, reduced costs, enhanced safety, and new business models.
Impact for Educational Institutions, Internet of things, Digital Enablers, New Age Production, Smart Factory, New digital industrial technology, Interdisciplinary Thinking, Digital Work Place, 3d printing,
Industry 4.0 refers to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, IoT, cloud computing and cognitive computing. Key features include interconnectivity, increased customization through 3D printing, integration of advanced analytics, reliance on cloud computing and use of autonomous robots. Challenges to implementing Industry 4.0 include high costs, lack of standards, security issues, disruptions to business models and potential job losses. For Bangladesh's garment industry, challenges include lack of government support, infrastructure and knowledge as well as availability of cheaper labor. Industry 5.0 is emerging as a future trend focused on closer human-machine cooperation and waste prevention.
This document appears to be a presentation about cyber-physical systems and Industry 4.0. It includes definitions of cyber-physical systems and Industry 4.0, examples of companies implementing Industry 4.0 solutions, discussions of the Internet of Things, impacts of new technologies on the economy, business, society and individuals, and slides on various related topics without titles like applications, history, and use cases. The presentation touches on many aspects of emerging digital technologies and their integration with physical systems and processes.
Industry 4.0 promises great increase in productivity and profitability. This presentation covers the basics of this new manufacturing approach and it separates facts from fiction.
The fourth industrial revolution Industry 4.0 represents a new paradigm shift from “centralized” to “decentralized” industry relies on cyber-physical based automation where sensors send data directly to the cloud and services such as monitoring, control and optimization automatically subscribe to necessary data in real-time. In the coming years, these technologies will be seen as a viable alternative to current manufacturing processes. According to a recent report by Markets and Markets, smart factory technology will have global market size of 74.80 Billion USD by 2022. The talk provides a comprehensive introduction to Industry 4.0 and Smart Factory. Technical challenges and social implications of smart factory will be discussed. The applicability of these emerging technologies in developing economies is highlighted in this talk as well.
Smart manufacturing 4.0 journey and strategy colin koh lkh precicon 23 sept 2021Colin Koh (許国仁)
This document discusses strategies for digital transformation and smart manufacturing 4.0. It introduces concepts like the VUCA world and the four industrial revolutions leading to increased automation. It presents frameworks for assessing maturity and ecosystems in smart manufacturing. Finally, it outlines a roadmap for planning digital transformation with an initial focus on the current state, then increasing speed, scale and managing change over time.
This document discusses Industrie 4.0, the fourth industrial revolution bringing connectivity and intelligence to manufacturing through technologies like the Internet of Things. Key concepts are connecting physical devices to networks and machines interacting with each other to enable mass customization. This transformation integrates horizontal and vertical networking in factories. Six design principles are outlined: interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity. Diagrams show examples of smart factories and supply chains enabled by Industrie 4.0.
The document discusses Internet of Things (IoT) and how it is enabling smart cities. It describes technologies that enable IoT like cheap sensors, bandwidth, processing power, and wireless coverage. It discusses the history and challenges of IoT. It outlines how IoT can be used across various sectors and environments like transportation, infrastructure, manufacturing, agriculture and more. It discusses how IoT can provide benefits like improved efficiency, reduced costs, and new revenue streams for cities. Finally, it discusses how citizen engagement and mobile applications can help build smart cities and provide solutions using IoT.
This document discusses Industry 4.0, which refers to the current trend of increased automation and data exchange in manufacturing technologies using cyber-physical systems, the internet of things, cloud computing, and cognitive computing. It is considered the fourth industrial revolution. The document provides an overview of the four industrial revolutions from the introduction of steam power in Industry 1.0 to the increased automation using sensors and machine learning in Industry 4.0 today. It also discusses key aspects of Industry 4.0 like cyber-physical systems, the internet of things, benefits and examples of IIoT (industrial internet of things) systems.
This document discusses Industry 4.0, the current fourth industrial revolution driven by cyber-physical systems. It outlines the evolution of previous industrial revolutions from mechanization to automation and digitalization. Key elements of Industry 4.0 include the industrial internet of things, cybersecurity, cloud computing, cognitive analytics, and smart factories. Examples are given of companies like Siemens, Trumpf, and GE implementing Industry 4.0 technologies. Potential impacts discussed include increased productivity, new business models, and changes to the nature of work and skills needed for the future.
AI for Manufacturing (Machine Vision, Edge AI, Federated Learning)byteLAKE
Artificial intelligence and machine learning technologies are transforming key industries like manufacturing, finance, retail, and healthcare. Edge computing and federated learning are emerging approaches that can help address challenges around data privacy, bandwidth constraints, and latency. Edge AI runs optimized models directly on devices to analyze data and only send results rather than raw data. Federated learning leverages local AI models across edge devices to improve performance while keeping sensitive data private. Together these approaches help make AI more scalable, responsive and privacy-preserving for industries.
Industry 4.0 focuses on technologies like digital twins, 3D printing, big data, augmented reality, autonomous robots, artificial intelligence, and cloud computing. Industry 5.0 emphasizes collaborative partnerships between humans and smart systems, with each focusing on their strengths. It also aims for mass personalization of products and a bottom-up supply chain approach. The goal is a more human-centric and sustainable system that improves productivity while maintaining human roles. Industry 5.0 provides purpose and responsible application of Industry 4.0 technologies, not a revolution, with the true 5th industrial revolution involving biological technologies.
The document discusses Industry 4.0, which involves trends towards automation and data exchange in manufacturing through technologies like cyber-physical systems, the internet of things, cloud computing, and artificial intelligence. It describes the key components of Industry 4.0 like system integration, simulation, big data analytics, autonomous robots, and cyber security. The applications and benefits of Industry 4.0 are also summarized, including increased productivity, flexibility to meet customer needs, and potential employment growth through demand for new technical skills.
Industry 4.0 promises to create new customer value in the market place by unleashing a combination of new technologies, data analytics, new generation cyber-physical production systems and newer methods of human machine interfaces. What does a developing country like India need to do to join the race?
Industry 4.0 has widespread application across Industries (Manufacturing, Logistics, Mobility etc.). In case of manufacturing and processing industries Industry 4.0 means Smart Manufacturing using IIoT (Industrial Internet of Things or simply Industrial IoT) in a connected smart factory.
It enables an Organization to make smart data-driven decisions based on Big Data, Artificial Intelligence and Machine Learning. Industry 4.0 IIoT has several benefits such as Resource Optimization, Cost Reduction, Automation, Predictive Maintenance and Prescriptive Analytics and Control etc.
This document discusses Industry 4.0 and smart manufacturing. It describes how Industry 4.0 involves integrating smart devices, turning products into smart products, and transforming factories into smart, connected factories. Key aspects of Industry 4.0 include products being described by models and having standardized network interfaces. The document outlines benefits of Industry 4.0 such as helping companies keep production in countries like India and compete globally through more efficient, customized production. Barriers and enablers to smart manufacturing are also presented, such as integrating customer data and demand across supply chains.
Digital Twin - What is it and how can it help us?Shaun West
RQ: What services can be provided (by whom and to who) through (or adopting, or developing) the digital twin concepts?
Our focus is long-life capital equipment
Consider the whole life cycle
Apply Service Dominant Logic in the assessment
Consider technical and business hierarchies
How the Digital Transformation is going to change the world of Work 4.0 with respect to the Introduction of Industry 4.0 technology. Will Jobs reduce or we will have more jobs with higher pay. An interesting analysis.
Do you know what is Industry40 and what can it bring to the business? Some companies miss out on huge opportunities and stay behind the competition, ignoring technological trends and innovations. Don't stay away, this presentation will show you the opportunities that the 4th industrial revolution brings to business!
If you are ready to know more – check out our article about Industry 4.0! Follow the link - https://bit.ly/2LH3yag
Industry 4.0 refers to the fourth industrial revolution driven by four disruptions: exponentially growing data and computing power, new analytics capabilities, advanced human-machine interaction, and improvements in transferring digital instructions to the physical world. Key aspects of Industry 4.0 include smart manufacturing platforms that enable data and resource sharing, advanced customization enabled by digital technologies like 3D printing, pay-per-use business models, smart connected products and machines, predictive maintenance using sensors and analytics, and new digital business models focused on services rather than products. While the impacts will be significant, changing industrial operations will likely take time as factories have long investment cycles.
A smart commercial building uses advanced IoT sensors to collect data from building functions and subsystems. This data is integrated into a Building Management System (BMS) that building operators can use to automate, control, and optimize building performance. Some key benefits of smart commercial buildings include improved energy efficiency, lower operating costs, and better tenant experiences through use cases like HVAC, lighting, security, and maintenance management. However, transforming older buildings and optimizing existing smart buildings presents challenges related to data integration across different systems and ensuring reliable connectivity.
This document discusses a wireless home automation system using the Internet of Things (IoT). It begins with an abstract that defines IoT as connecting physical devices to the internet to collect and share data. It then discusses how home automation is gaining popularity due to advances in automation technology and the widespread use of the internet. A wireless home automation system using IoT allows users to control home functions and appliances remotely using computers or smartphones. The system aims to reduce energy usage and human effort. Key advantages of a wireless system over wired include lower cost, easier expansion, and the ability to integrate mobile devices.
Industry 4.0 promises great increase in productivity and profitability. This presentation covers the basics of this new manufacturing approach and it separates facts from fiction.
The fourth industrial revolution Industry 4.0 represents a new paradigm shift from “centralized” to “decentralized” industry relies on cyber-physical based automation where sensors send data directly to the cloud and services such as monitoring, control and optimization automatically subscribe to necessary data in real-time. In the coming years, these technologies will be seen as a viable alternative to current manufacturing processes. According to a recent report by Markets and Markets, smart factory technology will have global market size of 74.80 Billion USD by 2022. The talk provides a comprehensive introduction to Industry 4.0 and Smart Factory. Technical challenges and social implications of smart factory will be discussed. The applicability of these emerging technologies in developing economies is highlighted in this talk as well.
Smart manufacturing 4.0 journey and strategy colin koh lkh precicon 23 sept 2021Colin Koh (許国仁)
This document discusses strategies for digital transformation and smart manufacturing 4.0. It introduces concepts like the VUCA world and the four industrial revolutions leading to increased automation. It presents frameworks for assessing maturity and ecosystems in smart manufacturing. Finally, it outlines a roadmap for planning digital transformation with an initial focus on the current state, then increasing speed, scale and managing change over time.
This document discusses Industrie 4.0, the fourth industrial revolution bringing connectivity and intelligence to manufacturing through technologies like the Internet of Things. Key concepts are connecting physical devices to networks and machines interacting with each other to enable mass customization. This transformation integrates horizontal and vertical networking in factories. Six design principles are outlined: interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity. Diagrams show examples of smart factories and supply chains enabled by Industrie 4.0.
The document discusses Internet of Things (IoT) and how it is enabling smart cities. It describes technologies that enable IoT like cheap sensors, bandwidth, processing power, and wireless coverage. It discusses the history and challenges of IoT. It outlines how IoT can be used across various sectors and environments like transportation, infrastructure, manufacturing, agriculture and more. It discusses how IoT can provide benefits like improved efficiency, reduced costs, and new revenue streams for cities. Finally, it discusses how citizen engagement and mobile applications can help build smart cities and provide solutions using IoT.
This document discusses Industry 4.0, which refers to the current trend of increased automation and data exchange in manufacturing technologies using cyber-physical systems, the internet of things, cloud computing, and cognitive computing. It is considered the fourth industrial revolution. The document provides an overview of the four industrial revolutions from the introduction of steam power in Industry 1.0 to the increased automation using sensors and machine learning in Industry 4.0 today. It also discusses key aspects of Industry 4.0 like cyber-physical systems, the internet of things, benefits and examples of IIoT (industrial internet of things) systems.
This document discusses Industry 4.0, the current fourth industrial revolution driven by cyber-physical systems. It outlines the evolution of previous industrial revolutions from mechanization to automation and digitalization. Key elements of Industry 4.0 include the industrial internet of things, cybersecurity, cloud computing, cognitive analytics, and smart factories. Examples are given of companies like Siemens, Trumpf, and GE implementing Industry 4.0 technologies. Potential impacts discussed include increased productivity, new business models, and changes to the nature of work and skills needed for the future.
AI for Manufacturing (Machine Vision, Edge AI, Federated Learning)byteLAKE
Artificial intelligence and machine learning technologies are transforming key industries like manufacturing, finance, retail, and healthcare. Edge computing and federated learning are emerging approaches that can help address challenges around data privacy, bandwidth constraints, and latency. Edge AI runs optimized models directly on devices to analyze data and only send results rather than raw data. Federated learning leverages local AI models across edge devices to improve performance while keeping sensitive data private. Together these approaches help make AI more scalable, responsive and privacy-preserving for industries.
Industry 4.0 focuses on technologies like digital twins, 3D printing, big data, augmented reality, autonomous robots, artificial intelligence, and cloud computing. Industry 5.0 emphasizes collaborative partnerships between humans and smart systems, with each focusing on their strengths. It also aims for mass personalization of products and a bottom-up supply chain approach. The goal is a more human-centric and sustainable system that improves productivity while maintaining human roles. Industry 5.0 provides purpose and responsible application of Industry 4.0 technologies, not a revolution, with the true 5th industrial revolution involving biological technologies.
The document discusses Industry 4.0, which involves trends towards automation and data exchange in manufacturing through technologies like cyber-physical systems, the internet of things, cloud computing, and artificial intelligence. It describes the key components of Industry 4.0 like system integration, simulation, big data analytics, autonomous robots, and cyber security. The applications and benefits of Industry 4.0 are also summarized, including increased productivity, flexibility to meet customer needs, and potential employment growth through demand for new technical skills.
Industry 4.0 promises to create new customer value in the market place by unleashing a combination of new technologies, data analytics, new generation cyber-physical production systems and newer methods of human machine interfaces. What does a developing country like India need to do to join the race?
Industry 4.0 has widespread application across Industries (Manufacturing, Logistics, Mobility etc.). In case of manufacturing and processing industries Industry 4.0 means Smart Manufacturing using IIoT (Industrial Internet of Things or simply Industrial IoT) in a connected smart factory.
It enables an Organization to make smart data-driven decisions based on Big Data, Artificial Intelligence and Machine Learning. Industry 4.0 IIoT has several benefits such as Resource Optimization, Cost Reduction, Automation, Predictive Maintenance and Prescriptive Analytics and Control etc.
This document discusses Industry 4.0 and smart manufacturing. It describes how Industry 4.0 involves integrating smart devices, turning products into smart products, and transforming factories into smart, connected factories. Key aspects of Industry 4.0 include products being described by models and having standardized network interfaces. The document outlines benefits of Industry 4.0 such as helping companies keep production in countries like India and compete globally through more efficient, customized production. Barriers and enablers to smart manufacturing are also presented, such as integrating customer data and demand across supply chains.
Digital Twin - What is it and how can it help us?Shaun West
RQ: What services can be provided (by whom and to who) through (or adopting, or developing) the digital twin concepts?
Our focus is long-life capital equipment
Consider the whole life cycle
Apply Service Dominant Logic in the assessment
Consider technical and business hierarchies
How the Digital Transformation is going to change the world of Work 4.0 with respect to the Introduction of Industry 4.0 technology. Will Jobs reduce or we will have more jobs with higher pay. An interesting analysis.
Do you know what is Industry40 and what can it bring to the business? Some companies miss out on huge opportunities and stay behind the competition, ignoring technological trends and innovations. Don't stay away, this presentation will show you the opportunities that the 4th industrial revolution brings to business!
If you are ready to know more – check out our article about Industry 4.0! Follow the link - https://bit.ly/2LH3yag
Industry 4.0 refers to the fourth industrial revolution driven by four disruptions: exponentially growing data and computing power, new analytics capabilities, advanced human-machine interaction, and improvements in transferring digital instructions to the physical world. Key aspects of Industry 4.0 include smart manufacturing platforms that enable data and resource sharing, advanced customization enabled by digital technologies like 3D printing, pay-per-use business models, smart connected products and machines, predictive maintenance using sensors and analytics, and new digital business models focused on services rather than products. While the impacts will be significant, changing industrial operations will likely take time as factories have long investment cycles.
A smart commercial building uses advanced IoT sensors to collect data from building functions and subsystems. This data is integrated into a Building Management System (BMS) that building operators can use to automate, control, and optimize building performance. Some key benefits of smart commercial buildings include improved energy efficiency, lower operating costs, and better tenant experiences through use cases like HVAC, lighting, security, and maintenance management. However, transforming older buildings and optimizing existing smart buildings presents challenges related to data integration across different systems and ensuring reliable connectivity.
This document discusses a wireless home automation system using the Internet of Things (IoT). It begins with an abstract that defines IoT as connecting physical devices to the internet to collect and share data. It then discusses how home automation is gaining popularity due to advances in automation technology and the widespread use of the internet. A wireless home automation system using IoT allows users to control home functions and appliances remotely using computers or smartphones. The system aims to reduce energy usage and human effort. Key advantages of a wireless system over wired include lower cost, easier expansion, and the ability to integrate mobile devices.
A Review: The Internet of Things Using Fog ComputingIRJET Journal
Fog computing is a new computing paradigm that processes data and analytics at the edge of the network, rather than sending all data to a centralized cloud. This helps address issues with the cloud-based Internet of Things (IoT) model, such as high latency, bandwidth constraints, location awareness, and mobility. Fog computing brings computing resources closer to IoT devices and end users by using edge devices like routers, switches, and access points as "fog nodes" that can perform analytics and decision making. This allows time-sensitive IoT applications to function more efficiently. Fog computing also helps optimize resource usage by balancing processing between the edge and cloud.
This document describes a system for monitoring weather conditions in a greenhouse using IoT technology. Sensors measure temperature and humidity and send the data via an Arduino Uno microcontroller to a web server. Users can then access the sensor data through the internet from anywhere in real-time. The system automatically controls devices like sprinklers or coolers if the sensor readings exceed set thresholds, and sends alerts to users. The technology allows for low-cost and flexible monitoring and control of greenhouse conditions without needing a dedicated server.
Accelerated adoption of Internet of Things (IoT) with In-network computing an...Infosys
In-network computing gives you the ability to compute at a particular point in the network where it can deliver maximum value. This opens new avenues of how applications and services are conceptualized or implemented, harvesting the benefits of distributed computing. In-network computing has significant benefits for the network infrastructure as it improves latency for end user/ devices while it also reduces the network traffic to a great extent. Emerging technologies like IoT and its application can immensely benefit by using In-network computing technology in conjunction with cloud technologies.
This document contains lecture notes on cloud computing from Jeppiaar Institute of Technology. It discusses the introduction to cloud computing, including definitions, evolution, and underlying principles. The key points covered are:
- Cloud computing allows for provisioning of computational resources over a network on-demand in a self-service model.
- It has evolved from earlier technologies like grid computing, utility computing, and virtualization. Hardware has progressed from vacuum tubes to integrated circuits.
- Cloud computing is built on distributed computing principles and delivers IT services through virtual servers over the internet. Resources can be dynamically scaled based on need.
IoT business models for Utilities? Here they are!Lemonbeat GmbH
Two examples of IoT business models for utilities are described:
1) A "Smart Energy for everyone" model that provides cost-optimized meter reading and customer services even in unregulated markets using IoT technology.
2) An "Energy management services via lean building automation" model that provides remote monitoring and parameterization of building assets like meters, heating systems, and pumps to optimize processes and costs for real estate companies.
The document then provides more details on a lean metering solution and lean building management solution that utilities could offer using IoT connectivity and analytics.
Making Actionable Decisions at the Network's EdgeCognizant
With the vast analytical power unleashed by the Internet of Things (IoT) ecosystem, IT organizations must be able to apply both cloud analytics and edge analytics - cloud for strategic decision-making and edge for more instantaneous response based on local sensors and other technology.
This document describes an automatic color sorting project using an Arduino Uno and color sensor. It includes chapters on embedded systems, hardware components like the power supply, microcontroller, motor, sensors, and software. The hardware is powered by a regulated 5V supply. It uses a microcontroller, color sensor, and conveyor belt to sort objects by color. The software controls the system and algorithms sort objects. Overall it presents a project to automatically sort objects by color for applications like industrial automation.
Future of Networking (5G) and The Impact on Smart Manufacturing and I.R 5.0Fabian Morais
5G will significantly impact smart manufacturing and Industry 4.0/5.0. Currently, smart factories use systems like MES and automated warehouses to track inventory and prioritize orders. However, 5G will enhance this by allowing IoT devices to locate resources faster in large facilities and handle multiple requests simultaneously. 5G will also allow single board computers with built-in sensors to more quickly exchange data between machines and a central management system. This real-time communication and monitoring enabled by 5G's high speeds and low latency will optimize production processes and help manufacturing stay efficient, flexible, and responsive to changing demands.
This document discusses green computing strategies for the Internet of Things (IoT). It begins by explaining how cloud computing services are used to meet the growing demands of IoT but require large energy-consuming data centers. Green computing aims to reduce the energy consumption of IoT devices and computing infrastructure without compromising performance. The document then evaluates aspects of green computing for IoT, including key concepts like edge computing. It also analyzes challenges of green computing implementation and potential solutions. The document concludes that green computing practices can help build a more sustainable IoT ecosystem by reducing energy usage across devices and infrastructure.
REASONS FOR THE INFORMATION PROCESSES UTILIZATION IN THE ERA OF THE PLATFORM ...IAEME Publication
Platform Industry 4.0 combines industrial technologies and the global network into a single Internet of Things. Cyber-physical systems will increase manufacturing efficiency and resource performance, and will also induce more flexible work management models. Every year, mankind has to work with increasing volumes of information. Therefore, the developers of interfaces and specialized software invent new ways of data display. But the work of a human-operator is still needed, as the specialists in this field know the mathematical environment, and therefore, they study the customer's business and help to compose the necessary algorithm of data analysis for the customer, which is still impossible for the computer. Thus, the aim of the research is to analyze the prospects for the development of post-NGN network technologies (environmental management, the creation of an integrated info communication space, the interpenetration of ideas and technologies of automation and telecommunications) in the era of the technology platform Industry 4.0.
IRJET- Automated Smart Greenhouse Environment using IoTIRJET Journal
This document describes an automated smart greenhouse system using Internet of Things (IoT) technology. Sensors are used to monitor environmental parameters like temperature, humidity, soil moisture, light intensity, and pH levels. An Arduino microcontroller collects data from the sensors and sends it over WiFi or Ethernet to a cloud server. Users can access the sensor data through a mobile app to remotely monitor greenhouse conditions. If any parameters exceed a threshold, the system can automatically control actuators like fans, pumps, and lights to regulate the greenhouse environment. The system aims to help farmers efficiently manage greenhouse agriculture without constant on-site presence.
The document discusses the Internet of Things (IoT), which connects physical objects through sensors and communication technologies. IoT allows objects like home appliances, vehicles, and industrial equipment to connect and exchange data. This emerging technology will consist of hardware devices, middleware for data storage and analytics, and interfaces to access and interpret data. Key enabling technologies for IoT include RFID and wireless sensor networks. Potential applications span many areas including transportation, healthcare, home automation, utilities, and more. Security, privacy, data management, and network protocols will need to be addressed for IoT to reach its full potential.
The document discusses Internet of Things (IoT) fundamentals including what IoT is, its genesis, how it relates to digitization, examples of IoT data analysis, and the impact of IoT. It then covers specific IoT applications and uses cases such as connected roadways, factories, buildings, and living creatures. It also discusses challenges with IoT such as network architecture, security, data management, and the convergence of IT and OT networks.
IRJET- Effect of ICT Application in Manufacturing IndustryIRJET Journal
This document discusses the application of information and communication technologies (ICT) in manufacturing industries. It begins by defining industrial informatics as the application of IT tools and techniques to solve real-world problems in industrial settings. It then discusses how ICT can be applied across different types of industries from primary to quaternary. Key applications of ICT in manufacturing mentioned include process modeling, production scheduling and control, and knowledge management. The document also examines communication requirements and challenges at different industrial levels from machines to components. It explores opportunities for using wireless technologies and computational intelligence techniques to improve real-time capabilities and decision making in industrial settings.
The document discusses the need for Industry 4.0 and connectivity in smart factories. It outlines 10 benefits of Industry 4.0, including helping manufacturers with challenges, increasing speed of innovation, and enabling sustainable prosperity. It describes how connectivity allows a planner in Europe to address a machine issue in the US in real-time. Smart factories require connected assets to generate real-time data for decision making. Integration of data enables efficiency across the entire supply chain. The document also discusses factors to consider when choosing an IoT connectivity solution and examples of connectivity types.
Revue de presse IoT / Data du 26/03/2017Romain Bochet
Sommaire :
- From the Edge To the Enterprise
- The Internet of Energy: Smart Sockets
- Google's big data calculates US rooftop solar potential
- Energy management: Oracle Utilities launches smart grid and IoT device management solution in the cloud
- Are vehicles the mobile sensor beds of the future?
IRJET-The Internet of Things Applications for Challenges and Related Future T...IRJET Journal
The document discusses the Internet of Things (IoT), including its applications, characteristics, and future challenges. Some key points:
1) The IoT allows objects to be connected and exchange information over the Internet. This enables applications in areas like smart homes, cities, transportation, energy, and healthcare.
2) Examples of IoT applications discussed are smart cities, smart homes/buildings, and smart energy grids. These allow for improved infrastructure, transportation, energy monitoring and more.
3) Characteristics of the IoT include interconnectivity, heterogeneity, dynamic changes, enormous scale, safety, and connectivity. Everything can be connected through different networks and protocols.
According to a new Gartner report1, “Around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2022, Gartner predicts this
figure will reach 75%”. In addition to hosting new 5G era services, the other major network operator driver for edge compute and edge clouds is deploying virtualized network infrastructure, replacing many dedicated hardware-based elements with virtual network functions (VNFs) running on general purpose edge compute. Even portions of access networks are being virtualized, and many of these functions need to be deployed close to end users. The combination of these infrastructure and applications drivers is a major reason that so much of 5G era network transformation resolves around edge cloud distribution.
Similar to Industry 4 - A Comprehensive Guide (20)
Industry 4.0 is changing the Landscape of how we live in this world. And Education is undergoing a Paradigm change to keep up with the changing times. What should India do to change its education system is explained through examples.
Going beyond Industry 4.0, Smart cities and Smart Factory, Japan takes it to the next level of a super smart society by 2030. Encompassing all the above and making it clear and easily implementable. It is a step forward which needs to be understood by many.
Four years back, during my interactions with certain big groups, I had offered to set up the Hub and spoke model for pilot projects in Industry 4.0. I thought a few of them will accept and become the hubs for smart manufacturing in India. Support was readily available from external trade development councils. Then came "demonetization" and "GST" and all these big groups disappeared. Then I approached some of the SME groups and textile associations. But as was expected after a 6 hour presentation, they maintained silence. I was expected to bring in money from these external councils. Investment was a big factor. I was not even paid for these "free" presentations.
Cut to a few days back, after 4 years of silence, I was approached by some of the above whether we can have the Hub and Spoke model projects of Industry 4.0 in India. I offered them end to end solutions. And listed out the cost factor. To my surprise, with so much research material available on Industry 4.0 projects, why are we hesitant to spend money for something good baffles me. For eg. in today's age if I want to buy a mobile, will I buy a Smartphone or an old relic. Even the poor desire a smartphone. Then why as a factory owner, we shy away from investing in Industry 4.0 technologies.
Then what is the way forward is the question oft asked. So I gave them another "free" presentation. Hope they buy the smartphone at least now.
In 2017, the World Economic Forum recognized the potential of advanced manufacturing technologies. In 2018, from among more than 1,000 examined production facilities, 16
companies were recognized as Fourth Industrial Revolution leaders in advanced manufacturing for demonstrating step-change results, both operational and financial, across individual sites. They had succeeded in scaling beyond the pilot phase and their sites were designated advanced manufacturing “Lighthouses”. In 2019, 28 additional facilities were identified and added to the network, which now provides an opportunity for cross-company learning and collaboration, and for setting new benchmarks for the global manufacturing community.
Lighthouses have succeeded by innovating new operating systems, including in how they manage and optimize business and processes, transforming the way people work and use technology. These new operating systems can become the blueprint for modernizing the entire company operating system; therefore, how they prepare for scaling up and engaging the workforce matters.
Education4.0 - How Industry 4.0 is going to change the Education SystemWg Cdr Jayesh C S PAI
Education 4.0 is Empowering education to produce innovation. Students will work in peer-to-peer networks or organizations which are open and structurally liquid. They will be hired (and laid off) on demand or work as free agents. They will have to compete for employment on a global market. New skills and competencies will become more important such as non linear thinking, social and intercultural skills, self-management and self-competence. Universities would have to re-calibrate their strategies across all the levers for Edn to remain relevant in the age of Industry 4.0.
To those who want to know how Industry 4.0 began and why it began, an easy presentation highlighting all relevant points. There is a fundamental curiosity as to how it all started and where is it headed towards. And whether it will be useful. To those who are still waiting to accept the change, look at what happened to Nokia when the iPhone started. It is better to start implementing the small changes soon.
Drop by drop the ocean builds up. Similarly, small innovations build up to count in implementing Industrie 4.0 across the world.Presently there are more examples in German Factories but the other countries are fast catching up. All these small examples give a remarkable picture of how the world is changing. And also gives us a direction to how we should change our skill sets to meet the ever growing Knowledge Economy. For students, you get an idea where research work is headed. The examples of Applications of Industrie 4.0 will give an idea of how small drops of technology changes is building into an ocean of Innovative ideas across the Industrial Spectrum.
To beat the Industry 4.0 movement in Germany the South Koreans under the Creative Economy Engine Project started the MII 3.0. The basic motto is to develop the Industries especially the SME sector to Industry 4.0 standards but also include emphasis on areas where Korea is very strong such as automotive industries and Ship building Industries.
The revolution in Supply Chain Management is through Digital Technology revolution in Industry 4.0. it brings in Transparency and accountability into the system bringing waste down to minimal. Procurement 4.0, Transportation 4.0, Supply 4.0 or Logistics 4.0, Whatever we may call it is going to change the face of the Industry. Data Analytics is going to make every ones life easy.
India is on the cusp of a manufacturing revolution towards Industry 4.0 provided the Government and the Industry get together its acts. A number of policies require to be formulated and implemented especially in the SME sector. Not just announced and left for no one to understand and implement.
Hannover Messe 2017 is going to be a watershed for the Digital Technologies taking over the Manufacturing world like a storm. The presentation gives a detailed look into what the worlds largest exhibition is going to give a feel of.
Technology that is going to create a revolution in every Industry including Health care. What is it, what are the tools and what is the outcome?
NASA started the research on Twins due to space travel and the need to have real time feedback of components. Now it is extending to even Health care to having a Human twin.
The path to realization of Industry 4.0 involves a clear understanding of the ways in which the physical can inform the digital, and vice versa.
INDUSTRIE 4.0 connects embedded system production technologies and smart production processes to pave the way to a new technological age which will radically transform industry and production value chains and business models.
The document discusses key aspects of integrated industry and the fourth industrial revolution. It describes how industrial companies can generate their own power through decentralized energy grids. It highlights new technologies like smart factories that are fully networked and intelligent, additive manufacturing, industrial internet of things, human-machine collaboration, and smart robots. It also discusses challenges around data security, standards, and changing business models in this new integrated industrial landscape.
The Industrie 4.0 has 9 pillars of Technological transformation that one needs to know and understand first before they start implementing it in their company. From Big Data & Analytics to Autonomous Robots to Augmented reality, the whole world is changing.
From Why a Smart City to What in a Smart city and "How" in a Smart City. The importance of being connected in a growing Urbanisation world is crucial now. Sustainability is a crucial issue in an Urban city.
Where is Europe with respect to Industry 4.0 and what should European Countries have to do to reach the level of Industry 4.0 competence achieved by Germany. Will Europe loose out to China / India as a manufacturing superpower or will Europe with this effort in the SMEs bring a revolution in the digital age.
China trying to be a world manufacturing Superpower by 2025 with clear laid down policies. It has been recognised that Manufacturing sector is what would take China to even more greater heights.
The document outlines 6 new strategies for Taiwan to address challenges of a shrinking workforce and competitive pressures: 1) enhancing flagship industries' smart supply chains, 2) encouraging startups, 3) localizing production, 4) achieving autonomy in key technologies, 5) cultivating technical talents, and 6) injecting industrial policy tools. It discusses using smart automation technologies like robots, IoT, big data, and cyber-physical systems to achieve goals like man-machine coordination, extended machine uptime, flexible scheduling, shortened lead times, and zero inventory.
When a student asks you to explain Lean Mfg, it requires a simple way of explaining.
By giving examples, pictures and case studies, each Lean philosophy can be explained.
Lean in the real Lean sense.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
3. *
*The first started at the end of the 18th century
with the introduction of water and steam based
mechanical production.
*In the 20th century, the second phase was
defined by mass production and assembly lines
using electrical energy, supported by human
labour.
4. *
*Increased control and reliability was enabled through
electronics, information technology and automatic
production.
*High reliability was a must, since industrial deployments are
often related to critical processes. A malfunctioning could
potentially put lives at risk or induce structural damage. To
provide such high reliability, industrial deployments during
the third industrial era relied on costly and inflexible
network infrastructures.
*This suited the predominant paradigm of linear production:
Write highly detailed specifications, have a system integrator
implement them, get the line up and running, do some minor
adjustments, produce and finally tear everything down and
start over for the next product.
5. *
*Instead of the primary goal being to produce
ever larger volumes at decreasing costs, the new
challenge is to produce individualized products -
or at least an exploding number of variants - at
mass production costs.
*The new need for such customized, but mass
produced items, has driven the need to rethink
business models, strategy and organizational
structures.
6. *
*From highly optimized to highly flexible, asks for a more
agile way of manufacturing. The business drivers are
changing.
*From mass production to mass customization.
*From low-cost country sourcing to proximity sourcing.
*From distanced automation to human-machine interaction.
*With the rise of digital enabling technologies and novel
manufacturing techniques, industry could evolve.
*To connect the physical world with the virtual world, the
fourth industrial revolution established networked
intelligence, integrating the internet of things with the
manufacturing process.
7. *
*From a technological perspective, Industry 4.0 can be
summarized as the trend to incorporate computer aided
manufacturing with automation, wireless networks,
continuous data gathering, and artificial intelligence.
*A shift in paradigm. From incremental improvements on
existing systems, mechanics, electronics and low-level
control, to innovation in algorithms, data, connectivity
and usability.
* Digitally enabling technologies like big data, AI, and
5G are exponentially growing and thus driving Industry
4.0.
8.
9. *
*Whereas the latest, most intricate implementations of
Industry 4.0 might only be feasibly implemented by large
enterprise scale manufacturers, small and medium scale
manufacturers might have even more to gain. New
capabilities can either be production steps that could only be
performed manually before - like tasks requiring sensitive
force control - or production steps that could not be done at
all, e.g. certain parts produced by means of computer aided
drawing and additive manufacturing.
*However, they might be as simple as converting paper-based
processes to digital, pulling more sensor data from machines,
and running basic analytics on cloud stored data. Industry
4.0 is poised to affect the manufacturing industry across the
board.
10. *
*As physical processes are digitized (i.e. represented &
controlled in the cyber world), data becomes more and
more important. Concepts like ‘digital twin’ (creating a
digital replica of a physical entity, like a robotic system)
are used to optimize cyber-physical systems (embedded
systems integrating computation, communication and
physical processes).
*They can range in complexity, from a single
microcontroller chip to complex, multipart devices and
are enabling digital representation and control. Rather
than general purpose devices, they are often built for a
specific task, with relatively lower computational power
and low power wireless communication.
11. *
*Through the widespread deployment of sensors and smart devices
in current factories, massive amounts of data are gathered. These
datasets are referred to as big data.
*Big data is characterized across four properties: volume, velocity,
variety and value.
*Volume represents the generation and storage of large amounts of
data,
*Velocity refers to the renewal rate of data points and their timely
analysis.
*Variety indicates the types of structured and unstructured data
gathered from different sources.
*Last, value refers to the hidden information stored in these
datasets. To gain value for the end user, the data needs to be
converted using analysis into actionable insights that drive
business decisions.
12. *
*In the industry, big data is gathered through
sensors and CPS. It is extracted from industrial
processes, then stored, processed and analysed
through machine learning algorithms, and at the
end of the cycle translated back to the production
process.
13. *
*Smart sensor systems are a combination of a
sensor, microprocessor and a wireless
communication technology. A collection of those
is able to convert a wide variety of inputs (e.g.
temperature, pressure, humidity, weight, gas
displacement, vibrations) into data and transmit it
through the network.
14. *
*Vibration analysis is used to detect defects that could
lead to material failure. Due to their networking
capabilities, the sensors can work together, being placed
at multiple positions next to a vibrating plate. The
mechanical sensors are connected with optical sensors to
cross reference vibrational data with a visual inspection.
Although in-line devices for quality control (like
cameras) have been around for a long time, gains in
analysis speed and the resolution of sensor data now
make real-time defect control possible. In Industry 4.0
less humans are needed to stand by the line and examine
products. Error checking was always time consuming
and never watertight. Now it can be automated and
executed with more precision than humans can achieve.
15.
16. *
*An overwhelming amount of data can clog both
the gateway and cloud. This is one of the main
reasons to use a distributed computing
architecture that aims to process data streams at
their origin.
*A first wave of processing and filtering of the
incoming data is performed at the place where it
is gathered, relieving computing systems and
reducing latency.
*Defect analysis is forwarded to the cloud to
assist further inspection reports.
17. *
*Wireless communication technology is of key
importance to connect digital and physical
systems. Significant advances have been made in
sensor development to allow low-cost, efficient
communication protocols. Currently, the most
used protocol for wireless communication is the
WirelessHART (Highway Addressable Remote
Transducer) protocol.
18.
19. *
*The extent to which cyber-physical systems can
transfer and communicate data is significantly
increased through 5G networks. Newer networks
will have much higher capacity than current LTE
or wireless networks and transmission speeds are
promised to be a 100 times faster and have low
latency - of less than a millisecond. More so, it
manages to provide this capacity in a sensor
saturated environment (e.g., in a plant with 1000s
of devices).
20. *
*The local 5G networks in our factory make it possible
for intelligent production components to communicate
ad-hoc with each other – without having to install
fieldbus cables and configure the communication
participants. This makes it easier to move and change
different components of the manufacturing process. It
also increases network reliability, and promises to lower
device cost and energy use.
*5G allows functions that were previously located at the
central control level to be moved to the edge nodes,
allowing controller systems to be leaner.
*Overall speed is increased through pre-processing and
security is enhanced through decentralized storage.
21. *
*Matrix production allows us to produce multiple
interchangeable parts on one single system, thus
allowing increased type variety, more frequent
changes of models, and quantity fluctuations in
production.
*There is no need to alter and optimize the entire
manufacturing floor. The design of the modular
systems and their placement on the floor can
simply be altered.
*Personal customization at the cost of mass
production.
22. *
*The increased importance of customization and
personalization have led to changes in behaviour for
consumers and producers alike. The concept of
computer aided manufacturing has long been a part of
the manufacturing process. Further advances in
modelling, simulation and computer aided design
tools, combined with continued development of
additive (3D printing) and subtractive (e.g. CNC
machining) manufacturing practices have made it
possible to build shapes and products that were
previously unfeasible, both physically and
economically.
23. *
*Real-time monitoring and feedback is assisted by
the digital twin
*It stands for the use of digital models of physical
objects to simulate the behaviour of a real
manufacturing process. It couples a physical process
with a digital equivalent for optimization in a virtual
setting. Real-world data, gathered from the print and
manufacturing process, is transmitted into the
modelled system to complete simulations, validate
the system, and dynamically adjust it where needed.
24. *
*Digital twins are a complex conglomerate of technical data,
combined to make a virtual technological representation of a
physical process or product. They simulate a product's entire life
cycle, predicting its behaviour, and optimising its design and
production system. They incorporate AI and machine learning
capabilities, data analytics and multi-physics simulations. They
demonstrate the potential impact of environmental or usage
conditions, design changes and many performance variables.
Digital twins are a cost-effective means of understanding the
performance characteristics of a real-world physical counterpart,
before any investment is made in actual prototypes. This cuts
down drastically on development time, and delivers a final
process or product of optimal quality.
25. *
*Product digital twins consist of a virtual link to a
model product that demonstrates how it performs
in various operating conditions, obviating the
necessity for multiple physical prototypes.
Product digital twins also improve the
manufactured product's final quality, and provide
a much faster and more effective response to
feedback.
26. *
*Production digital twins simulate production
processes, analysing and validating what is
happening and why, so as to create a more
streamlined production methodology that can be
optimised for different conditions. Sensory data
can be used for predictive maintenance, making
manufacturing operations faster, more reliable,
and ultimately more efficient.
27. *
*Performance digital twins capture huge
amounts of operational data in a product's
performance, analysing it with big data
analytics and acting on the information to
enable more informed decision-making.
28. *
*Customizable parts can now be mass produced on next
generation 3D printers.
*These are capable of performing real-time quality
analysis and real-time adjusting by using sensors and
computer vision. When the print is faulty, issues are
processed to optimize production.
*Materials are optimized for batch processing, and
through compact and modular design, the production
space needed for the printers is minimized.
*Advances in material science have led to printed parts
becoming as strong as injection moulded ones for
certain applications.
29. *
*Collaborative robots allow for new opportunities in which the
human worker is in the same workspace, with robotics systems
assisting with non-ergonomic, repetitive, uncomfortable or even
dangerous operations.
*A cobot can check, optimize, and document the results of its own
work while being connected to the cloud. Thanks to integrated
sensor and communication systems, cobots can directly
collaborate with their human “colleagues,” safely handle sensitive
products, and don’t require a protected space.
*In order to truly work together, they are programmed to ensure
that their behaviour can be tuned or altered by operators, and that
they’re increasingly aware of humans in situations where man and
machine are dependent on one another. This in contrast to ‘dumb’
industrial robots which will continue repeating pre-programmed
movements regardless of what’s in their path.
30. *
*Such collaborative working environments are further
supported by augmented reality (AR) and virtual reality
(VR) technology. AR/VR gives humans the ability to
display their steps, or ask for virtual support, either from
an AI or from remotely working human experts. They
can receive immediate visual feedback, reducing the
need to remember complex sequences.
*For e.g. AR goggles point the workers towards the
correct size and position of all the screws used in final
assembly.
31. *
How big data, advanced analytics,
and AI are changing manufacturing
32. *
*Data analytics for industrial processes has
traditionally relied on conventional statistical
modelling approaches. Companies in
manufacturing industries successfully integrated
engineering, science, and statistical modelling
tools to develop large-scale process automation
platforms. These systems are often known as
'Advanced-Process-Control' (APC) systems.
They enable companies to optimize the
efficiencies of their machines and processes.
33.
34. *
*The implementation of process automation
through APCs has often been restricted to large-
scale processes, primarily due to the large capital
expenses associated with equipment purchases
and installation, and the return on investment
(ROI) is rarely favourable for small-scale
processes. In addition, the manufacturing sector
has lagged behind other sectors in investing in IT
systems that enable them to capitalize on their
data to increase efficiencies in small scale
processes.
35. *
*Several technological advances have changed this
pattern of lacking IT investment in the manufacturing
sector. Advances in computational infrastructure,
particularly cloud-based platforms, have enabled
efficient storage and management of large data
volumes.
*Enterprise Resource Planning (ERP) systems, which
were traditionally used to manage back office functions,
have become web-based, enabling the integration of
main business processes with collaborative functions
such as Supply Chain Management (SCM), Product
Lifecycle Management (PLM) and Customer
Relationship Management (CRM).
36. *
*Low-cost and long-lasting sensors can now be
connected through wireless networks, enabling large
amounts of data from the factory floor to be collected in
a scalable and cost-effective manner. Information from
IoT devices in the factory can be combined with wider
enterprise datasets to optimize productivity and
efficiency in a highly flexible manner in response to
demand. The reduced cost of intelligent sensors,
accessible software, and advances in analytical tools has
made room for a more bottom up approach to
manufacturing automation. Insights and intelligence can
now be achieved on much smaller scales, opening up the
concepts of Industry 4.0 to smaller enterprises.
37.
38. *
*The main difference in data analytics between
the third and fourth industrial revolution is the
shift to a more proactive approach that
anticipates problems before they occur and
promotes corrective actions ahead of time.
*Algorithms can be descriptive ("what has
happened"), diagnostic ("why an event has
happened"), predictive ("what will happen"), or
prescriptive (suggesting a course of action).
39. *
*Of the general population of big data, most sets are unstructured
(about 95%, text, images, audio and video are all common
examples.
*In order to prove value, there is a need to organize and structure
datasets, to homogenize the data that reside in different systems
and sources (e.g., sensors, automation devices, business
information systems). Or, to improve the algorithms to better
adapt to heterogeneity.
*These are the challenges for more advanced analytics. Due to high
volume, the size of big data sets can create the opportunity to
study data from heterogeneous sources. But due to the massive
samples, conventional statistical methods can be outdated.
*Furthermore, computational methods, that have worked fine on
smaller samples, might fall short in efficiency on these new, larger
datasets.
40. *
*The process of identifying factors that drop
efficiency, or cause defects or quality deviations
in the manufactured product is called root cause
analysis (RCA).
*Traditionally manufacturers rely on on-site
expert knowledge for this. And while experience
is valuable, production lines are often so complex
that awareness of every component and sub-
process and their relationships is humanly
impossible.
41. *
*In Industry 4.0 automated root cause
analysis can be implemented to evolve from
reactive and preventive to predictive practices.
Beyond increasing accuracy and shortening
investigation times for problems, automating root
cause analysis can inform predictive maintenance
and predictive quality control.
42. *
*Identifying process causality in fault detection can be
difficult when relying solely on process knowledge or
experience. Predictive maintenance analyses big data
of the historical performance of equipment or
production lines to forecast future failures and limit
downtime. Here, big data, is the field that treats ways to
analyse and extract information from data sets that are
too large or complex to be dealt with by traditional data-
processing techniques. This type of structural
monitoring replaces manual inspections that require
human intervention otherwise needed to prevent
equipment failure.
43.
44. *
*Aims to improve production quality and reduce
costs through predictive alerts and automated
anomaly detection. Advanced analytics may also aid
in determining the variables that have the highest
impact on quality issues, helping to prioritize issues.
Real time operational visibility enables engineers to
drill down into any individual machine and its
sensors, to determine its impact of overall quality
levels. Predictive quality alerts may be formulated
based on business rules combined with automated
anomaly detection, again to reduce human error and
downtime.
45. *
*Data can be incorporated into automated data
analytics pipelines to enable simultaneous
analysis and visualization in real time. There are
several advantages to building automated data
analytics capabilities. Having greater visibility of
processes in real time enables engineers to
manage systems more effectively by detecting
when key performance indicators (KPIs) deviate.
*In these cases, corrective action can be
implemented faster to resolve issues
46. *
*PPH tools analyse all the relevant variables that impact
the total profitability of a manufacturing company. They
can take thousands of parameters within an integrated
supply chain and manufacturing environment and
provide intelligence on how best to capitalize on the
conditions.
*Data is combined from multiple sources that track
incoming raw materials, inventory, automated processes
performed by cyber-physical systems, and factory
outputs. Analysing these variables from across diverse
parts of the organization can diagnose specific problems
and aid in root cause analysis or find bottlenecks in
production lines.
47. *
*By analysing individual processes and process inter-
dependencies, supply chains can be optimized for
transportation times, assembly line flow rates, and
fluctuations in demand. Such YET analytics ensure
the most efficient operation of individual production
units during operation, helping to increase their
yields and throughput or to reduce the amount of
energy they consume. These insights can help to
build factory-wide flexibility by forecasting the
impact of disruptions or optimizing energy
consumption per individual unit.
48. *
*A trend towards eliminating defects from
manufacturing processes and may take several
approaches including detection, repair,
prediction, and prevention.
49. *
*Early work in AI applied first-order logic (a statement
may be true or false depending on the values of its
variables, also called 'predicate logic') to the operational
management of computerized production processes.
*This approach permits a descriptive and non-procedural
representation of knowledge related to the operation of a
production line. The user only had to specify what to do
and not how to do it. Many of the early examples of AI
were characterized by structured contents (data that
adheres to a relational model that can be analysed) and
centralized control structures.
50.
51. *
*To mimic human-like behaviour in an automatized
process, AI must be able to adapt to, and extract
intelligence from the widest sources of data possible.
*Whether it be text-based, visual, auditory or anything
else that might hold information suitable for processing.
Manufacturing environments are dynamic and decision
problems are often unstructured. As the systems
continuously change, logic operations for manipulating
processes must be continuously reviewed.
*Fortunately, current AI tools have reached the inherent
capability to respond to dynamic conditions within
manufacturing settings.
52. *
*ML algorithms can be classified according to the broad
approaches employed in algorithm
development: unsupervised learning, supervised
learning and semi-supervised learning. The first,
unsupervised learning, looks for previously undetected
patterns in a data set with no pre-existing labels and with
a minimum of human supervision, the second maps an
input to an output based on example input-output pairs,
while the last combines a small amount of labelled data
with a large amount of unlabelled data during training.
53.
54. *
*Application of these models has been made possible by the
availability of large datasets and the required computation
infrastructure for training and deployment. Unsupervised and
supervised ML approaches are widely implemented in process
industries, accounting for between 90%-95% of existing
applications.
*ML can be used as a predictive modelling tool for both process
and quality control. Different ML algorithms are suitable for
different processes: For example, dynamic relationships between
process data are particularly useful for process optimization.
Hierarchical multilevel models may be used to describe the
relationships between different key performance indicators
(KPIs). Lastly, distributed ML can be leveraged to analyse metrics
across diverse machines and processes with different data types
and sampling frequencies.
55. *
*DL is a subset of machine learning, but contrary to basic
machine learning, deep learning networks often rely on
multiple layers of artificial neural networks (ANN), each
contributing to different interpretations of a dataset.
While basic machine learning models often need
guidance from an engineer, a deep learning model can
determine on its own whether a prediction is accurate or
not.
*DL has been effectively used in domains with complex
data such as image classification.
*Deep learning classification models have been used to
improve data collection and organization, leading to the
identification of possible defective products over
multiple assembly lines.
56.
57. *
*RL approaches learn dynamically by adjusting
actions based on continuous feedback
mechanisms to optimize a desired output.
*AI methods can be employed in isolation to
specific processes or in combination by applying
multiple methods sequentially or simultaneously.
58.
59. *
*A different AI subset, active learning, uses NLP to enable
knowledge transfer directly from humans to collaborative robots
(cobots).
*Human-robot collaboration has recently gained traction in many
manufacturing settings where they offer advantages in flexibility
and can lower production costs. Unlike traditional manufacturing
robots which are designed to work autonomously, cobots are
intended to interact with humans.
*For these interactions to succeed, the robot must recognize human
intentions. A recent study implemented a recurrent convolutional
neural network (RCNN)-based system that provides early
recognition of specific human activities.
*It has been employed to enable cobots to perform quality
inspection cases without the assistance of a human.
60.
61. *
*AI is influencing all aspects of Product Lifecycle
Management (PLM), including the design phase.
Increasingly Generative Design tools are used.
These algorithms automatically generate
optimized design options in 3D CAM modelling
programs for achieving a set of design goals.
Multiple iterations of a product can be designed
and compared using different metrics and
constraints. This enables engineers to quickly
generate a range of design options to filter and
select the ones that best meet their goals and
constraints.
62. *
*Automation of visual inspections
*During the manufacturing process damages such
as scratches or cracks can make the product
unusable for further processing or end up in the
final product.
*By combining a deep learning algorithm with
computer vision techniques, defects can be
detected in milliseconds, by quickly selecting a
faulty area, and then using DL to assist in
interpretation using heat maps.
63. *
*High quality, curated datasets are essential for training
ML models for specific scenarios, especially for neural
networks. However, the availability and scope of
datasets with appropriate quality standards is often
limited. Data must also be cleaned, and its quality
evaluated prior to applying the ML algorithms for model
development. Missing data, outliers, and any differences
between sample variables must be identified and
addressed appropriately.
*Because the use of AI systems depend on the precision
of their models, this data pre-processing is an important
and time-consuming step, requiring significant
computational and storage capabilities.
64. *
*In Industry 4.0 applications, algorithms must be very adaptable in order to
guarantee performance in real world settings. Both the training and scaling of
ML models is challenging because large models can involve millions of
parameters and large datasets. It requires the scalable storage, distributed
processing, and powerful computing capabilities that cloud infrastructures
provide.
*Clearly, the amount of data and computations required to make manufacturing
more intelligent comes at a price. Firstly, there are the literal costs of bandwidth
usage, data storage, and computing. Secondly, the large amount of data can
overwhelm a company’s networks and IT systems. A third problem is long and
unpredictable latency, resulting from sending data to a cloud datacentre and
performing computations there before sending information and commands back
to the factory floor. This is especially problematic for time-sensitive use-cases.
*A solution comes in the form of edge computing, and the concept of “AI at the
Edge.” In such a computing environment edge devices (e.g. smart sensors or
other industrial IoT devices) carry out a substantial amount of computation,
storage, and communication locally. The edge nodes either pre-process data
before transmitting it to the cloud, or execute the whole AI application locally
from input to output, enabling efficient real-time intelligence at the point of
need
65. Edge computing: from top to bottom: Cloud
computing, Edge servers, Edge devices.
66. *
*Current plug-and-play AI solutions are often
developed to address only specific problems.
Therefore, in most instances, companies must
employ experimental and agile approaches to
implementing AI tools more widely. Rapid
prototyping and small-scale deployment are
useful in determining the optimal analytical
approach prior to rolling out operational AI
systems.
*This also reflects the role of smaller enterprises,
and their possibilities in Industry 4.0.
67. *
*Furthermore, to realize the potential of advanced
analytics, manufacturers must focus on developing
capabilities and skills across the organization. To assist
the development of AI-based models, it is imperative to
build trust and transparency. One of those attempts is
Explainable AI (XAI), referring to methods and
techniques that apply AI such that its results can be
understood by human experts. This is in contrast to the
“black box” element of model design.
*In machine learning, even model designers cannot
explain why the AI arrived at a specific decision. XAI
aims to make algorithms self-explanatory.
68.
69. *
*Big data, advanced analytics, and artificial intelligence
tools have the potential to transform the manufacturing
sector. Advanced analytics can aid manufacturers in
solving complex problems as well as revealing hidden
bottlenecks or unprofitable processes. The exponential
increase in the volume of data available for analysis has
prompted the adoption of more sophisticated models,
leveraging advances in AI and computational
infrastructure. These technologies provide the link
between machine automation, information automation
and knowledge automation. Ultimately, the conversion
of data to insights will drive manufacturing
productivity, efficiency and sustainability in the near
future.
71. *
*Modern manufacturing environments pose several
challenges.
*Across a factory floor, there may be hundreds of cyber-
physical systems (CPS), including robotics, automated
devices for quality control, and actuation platforms.
Each asset or process could be associated with numerous
sensors, connecting the physical and virtual worlds by
translating real-world actions into data and vice versa.
Furthermore, advanced analytics convert these large
datasets into actionable insights in real-time. Continuous
monitoring and real-time analysis on this scale requires
dependability, resilience, security, and flexibility in the
design of sensors, network, and computing
architectures.
72. *
*A sensor node usually includes a transceiver
with an antenna for data transfer, a
microcontroller for controlling the sensors, and
an energy source. Sensor nodes are resource-
constrained, with limited computing power,
memory capacity, and battery life.
*Sensors vary in terms of resources, ranging from
simple devices that collect and transmit low data
volumes to complex designs.
73. *
*A sensor network is composed of a collection of sensor nodes.
Traditionally, in industrial settings, networks of sensors are
connected by wired infrastructure.
*Processes are monitored and controlled using established digital
technologies.
*These systems are reliable but time-consuming and expensive to
install and maintain. For large-scale manufacturing, the capital
costs of installation can be economical when connecting a
significant number of devices. However, for smaller
manufacturers, the upfront cost of installation is usually
prohibitive. These systems also lack flexibility and scalability, as
it is challenging to modify hardwired infrastructure
retrospectively.
*To enable Industry 4.0, reliable, flexible, and scalable
deployment, networking, and monitoring of a large number of
sensors is necessary.
74. *
*WSNs consist of spatially dispersed sensors for monitoring
physical parameters and disseminating collected data to a
central location. In WSN platforms, sensors connect to
computing infrastructure via wireless communications
technology. WSNs built for industrial environments share
essential characteristics, including low operating costs,
energy-efficiency, self-organization, and self-configuration.
*They should be able to be rapidly deployed in a manner that
allows for scalability, flexibility, and simple upgrading. Each
of these characteristics place design constraints on sensors
and networks such as dependability, IP connectivity, security,
low power consumption, standards compliance, and cost.
75.
76. *
*Contains more computational power compared
to earlier generations. These devices can process
data at the edge of the network, closer to where it
is generated and used. This reduces the
bandwidth and latency associated with data
transfer between devices, enabling real-time data
analytics. Sensor nodes may also connect with a
local network through a gateway that acts as a
bridge to the rest of the system. Gateway devices
typically have more resources than sensor nodes,
enabling data to be stored and processed closer to
the edge of the network.
77. *
*In recent years, significant progress has been
made in device design and standards such as
IEEE 802.15.4 and IEEE 802.15.1.
*IEEE 802.15.4 is the base technology for
standards such as
*ZigBeePRO,
*WirelessHART,
*ISA100.11a,
*and WIA-PA.
78. *
*ZigBee is a wireless transfer protocol used in
personal area networks requiring low power, low
data rate, and proximity. It is typically used in
applications that require long battery life, secure
networking, and limited data transmission.
79.
80. *
*Highway Addressable Remote Transducer is a
protocol that extends traditional HART
communications through wireless connectivity. It
is highly encrypted to protect data, with a more
extended range than Zigbee. Sensor nodes form a
flat mesh network in which every device serves
as both a signal source and repeater. This system
design enables automatic redirection of data in
the event of a disruption in the network.
81.
82. *
* It is a wireless standard developed specifically
for the automotive industry.
83.
84. *
*Wireless Networks for Industrial Automation
and Process Automation was developed by the
Chinese Industrial Wireless Alliance as a secure
alternative to wired communication protocols and
is popular in the industry in China. These
standards are designed for low power device
connectivity and have been successfully
deployed in process automation in the oil and
gas, chemical, pulp and paper, and glass and
mineral processing industries.
85.
86. *
*WSN systems can also be vulnerable to disparities in network
connectivity and function. Security attacks have been documented
on ZigBeePRO, WirelessHART, WIA-PA, and ISA100.11a
standards.
*Security concerns are an essential issue for any business, and new
research is focussing on designing network architectures and devices
to combat security breaches. Blockchain technology also offers
potential for improving security in remote monitoring applications
by applying cryptographic algorithms to ensure the confidentiality of
data on WSNs.
*Interference from devices complying with other standards operating
in the 2.4GHz range, such as IEEE 802.11, have also been observed.
These problems lead to transmission errors and inefficiencies from
energy depletion, increasing operational costs. Post-deployment
tools continuously monitor WSNs by detecting network, firmware,
or hardware issues.
87. *
*A classic problem that affects all IoT implementations is
the lack of interoperability between standards and
devices.
*IoT suppliers may have proprietary hardware, software,
and communications protocols. It becomes challenging
to collect, integrate, and contextualize data in these
settings.
*Integrating legacy equipment into IIoT environments
also represents a challenge for manufacturers. Older
generations of equipment and sensor nodes devices may
not be compatible with modern WSNs platforms in
software or hardware.
88.
89. *
*The 5G frequency spectrum is divided into three
classes based on bandwidth. Millimeter waves in
the high-band frequencies from 24 GHz to 72
GHz are capable of the fastest speeds reaching up
to 1–2 Gbit/s down. However, the signal reach is
short, requiring more cells to achieve coverage
and has difficulty in traversing solid structures.
Mid-band frequencies, between 2.4 GHz to 4.2
GHz, can reach speeds in a 100 MHz wide band
from 100–400 Mbit/s down. Finally, low band
classes use a similar frequency range as 4G
wireless technology.
90. *
*5G networks operate at higher frequencies
compared with 4G networks and support a higher
device density. The latency of 4G networks
averages 50ms and could reduce to 1ms with 5G.
As computing transitions from hardware
embedded within devices into the cloud, the
potential for increased performance, flexibility,
and cost-efficiency can be realized. The 5G
architecture also allows edge processing
resources hosted on a local network. This reduces
latency, enabling data to be analyzed and acted
on closer to the point of interest.
91. *
*In addition to security and speed, wireless
solutions should provide reliable coverage,
predictable latency, high device density, and
enable complete visibility and monitoring of
equipment and data. A modern manufacturing
setting could contain a high density of CPSs and
associated sensors. The sheer volume of sensors
and data generated requires substantial
bandwidth to transmit. Low latency is necessary
to facilitate rapid data transfer and analysis in
real-time.
92. *
*Moreover, low latency enables autonomous applications
that require fast response times. Ensuring that network
coverage and bandwidth are adequate to reduce latency
and interference issues is challenging, but if
implemented correctly, 5G eliminates the need for
enterprises to implement expensive cable infrastructure.
Due to flexible reconfiguration and the predicted
widespread adaptation, 5G is also expected to solve
interoperability issues. This offers further benefits in
security for data and device integrity and presents an
open platform for developing applications and services.
Furthermore, because costs are lowering, smaller
manufacturers can access these technologies easier than
ever before, enabling the democratization of industrial
manufacturing.
93.
94. *
*The plant produces 4G and 5G base stations, and uses Nokia’s
private (4.9G/LTE) wireless networks to enable connectivity
between their assets, to run analytics on edge computing systems
and to maintain a digital twin of their operating systems.
*By using autonomous mobile robots from Omron LD, the flow of
material to produce the base stations is now automated. By using
the network's low latency, high speed, and ability to connect
multiple devices, the mobile robots are able to transport
components to their needed location based on communication
with production line equipment. The improvements due to the 5G
related upgrades led to more than 30 percent productivity gains,
50 percent savings in time of product delivery to market, and a
reported annual cost saving of millions of euros.
97. Unimate, the very first industrial robot, was used to transport and weld die-castings
onto car bodies. 1961. Joseph Engelberger, engineers George Munson and Maurice Dunne
99. 6-jointed Rancho arm became one of the
first arms to be controlled by a computer
100.
101. *
*It used a programmable logic controller (PLC), a
type of computer adapted for the control of
manufacturing processes.
*Its purpose is to monitor an input, and make
decisions based on a custom program to control
the state of an output device. An example here
would be an input from some sort of sensor or
encoder sending an electrical signal to a motor,
moving by means of an AC drive to a certain
point at a programmed speed.
105. 1984, Unimation, the company originally
founded by Engelberg, was acquired by
Westinghouse Electric Corporation
106. *
*The development of robotics in manufacturing
was originally driven by the needs of the
automotive industry. The predominant areas of
research were kinematic calibration, motion
planning and control law.
107. *
*Improving the accuracy of a robot across its work
volume.
*Based on a mathematical model, the process of
calibrating is performed in four steps:
*After the model is made (1),
*sensors look for the discrepancies between math and
reality (2),
*parameters that differ from their nominal values are
identified (3)
*and the model is updated (4).
108. *
*the calculation of sub-goals to control the completion of a
robot’s spatial goal. The algorithms governing motion
control for robotics have been in development since the first
robot was built, but literature generally splits between two
types of algorithms: implicit and explicit.
*Implicit algorithms specify the behaviour of the robot. In
order to solve motion planning problems, the state of the
robot and the state of the environment (e.g., obstacles or
moving targets) are mapped to a set of inputs to control the
robot.
*Explicit methods on the contrary provide a path or trajectory
for the robot from some initial configuration to a target
configuration
109. *
*The motion of a robot, called a trajectory, in its essence consists
of a series of desired positions, velocities and accelerations.
*‘Standard’ techniques, like proportional derivative control, a
control output based on the difference between a set point and a
measured process variable is used to continuously apply
corrections.
*Applied nonlinear control, a method in which nonlinear forces
could be taken into control (e.g., Coriolis or centripetal forces
varying with the square of the speed of the robots motion).
*Adaptive control, in which adapting control laws could regulate
changing variables, to account for situations where, for example,
the weight of a carried object might decrease.
110.
111. *
*The adoption of wireless sensing networks provides
solutions for previous extensive and expensive wiring
throughout factories. As sensor and machine are further
digitized, data streams start growing. Different AI
techniques applied to industrial manufacturing provided
robots the ability to operate in more dynamic
environments, filled with more uncertain
tasks. Intelligence and flexibility are provided through
machine learning algorithms, using the larger
availability of data, allowing robots to learn from
previous selected examples and historical behaviour.
112. *
*By introducing a remote human operator in the
control loop through teleoperation, capabilities
are extended. The success of such a system is
dependent on communication. Direct control is
not possible when signal delays are too high,
demanding a robot to take charge when
commands are absent. Wireless, remote, and
smarter robots are needed and thus developed.
113. *
*A cobot is “an apparatus and method for direct
physical interaction between a person and a
general purpose manipulator controlled by a
computer.”
*To make robots safe enough to team up with
people, to develop ‘operator-assisted’ devices.
114. In 2004 after a long collaboration with the
German Aerospace Center Institute, Kuka
Robotics released the LWR cobot.
116. *
*Beyond increasing ergonomics, modern cobots strive for
increased human-robot collaboration in industrial
settings. They aim to merge robotic capabilities, such as
high levels of accuracy, speed and repeatability, with the
flexibility and cognitive skills of human workers.
Modern cobots can check, optimize, and document the
results of their own work while being connected to the
cloud. Thanks to integrated sensor and communication
systems, cobots safely handle sensitive products, and
don’t require a protected space.
117. *
*Safety and intuitive control are the guiding principles in
the design of cobots, which are programmed to ensure
that their behaviour can be tuned or altered as easily as
possible by operators. They are also increasingly aware
of humans in situations where man and machine are
dependent on one another.
*A modern cobot can detect contact immediately to limit
its force output. It does this by having joint torque
sensors in all axes. Intuitive control is enabled by the
ability to program it through simulation, hand-guiding
(showing a trajectory by hand) or touch commands on a
regular interface.
118. *
*In line with the decreasing costs of robotic arms, end-of-
arm tooling options are also decreasing in price, and
expanding in usability and variety. Developments
include new soft robotic grippers, innovative material
depositing tools, inspection tools, and assembly tools.
*Traditional mechanical grippers are becoming more
versatile, modular and lightweight. More exotic are
magnetic, suction, soft and vortex- (maintaining an air
gap) grippers. Through innovation in contact type,
manufacturers can avoid harm to sensitive, oddly shaped
or volatile products.
119.
120. *
*Industrie 4.0 is largely about a shift from conventional
mass production models to mass customization,
combining the flexibility and personalization of custom
products with the low unit costs expected from mass
production. Today, product customization is key to drive
sales and increase customer satisfaction. Not all
customers want the same products or use them the same
way. In the coming years we will increasingly see
personalized and more customized products, based on
production models like Made-to-Order (MTO),
Configure-to-Order (CTO) and Engineering-to-Order
(ETO).
121.
122. *Furthermore, thanks to the explosion of e-
commerce, manufacturers increasingly will be
able to produce exactly what is ordered, moving
towards the vision of lean manufacturing and
stock-less inventories. This will require flexible
and hyper-efficient production lines that can be
reconfigured fast to produce different products.
Reconfiguration, customization, and adaptation
allow systems to react quickly to production
changes.
*
123. *
*It is an integrated, computer-controlled complex of
numerically controlled machine tools, automated
material and tool handling and automated measuring and
testing equipment that can process any product to a
predetermined schedule with a minimum of manual
intervention and short change over time.
*It integrates smart sensors, adaptive decision making
models, advanced materials, smart design and
machinery.
*Flexible manufacturing frameworks enable companies
to produce a greater variety of products whilst reducing
delivery lead times and inventory requirements.
124. *
*Smart design processes, for instance, use techniques like
computer assisted design and manufacturing to interact
with product and prototypes in virtual and physical
reality. The entire life cycle of the product can be
monitored, simulated, and tracked.
*The paradigm changed significantly with the rise of
additive manufacturing (3D printing) technologies.
*Continuous improvements in both CAD and CAM
enable software and automation to be further immersed
within the product life cycle. Major developments have
been made to improve the flexibility, speed, volume, and
capabilities of these systems.
125. *
*3D printing technology specifically continues to
expand in its variety and capabilities. Speed is
directly connected to productivity, so
development of better printing heads gains much
attention, as well as developments in materials.
Factors like curing time and other material
properties play a big role in the printing process.
Furthermore, post-processing can be very time
intensive and delicate, making it a big factor to
increase production speed in the product cycle.
126. *
*Hybrid manufacturing combines in one machine additive
(3D printing) with subtractive techniques to leverage the
most valuable capabilities of both methods: The geometrical
complexity of additive manufacturing and the high precision
of subtractive processes.
*The technique is becoming a more common practice
especially for metal part machining in aerospace, medical,
and tool and die industries.
*Imagine a five axis robot, which can pick a 3D printing tool
from a toolbox to deposit metal. After printing, the robot can
change its tooling to a milling bit to start finishing the
surface of what has been printed. Changing a single end-tool
changes its function, making it a flexible manufacturing
system.
127. *
*Digital shadow stands for the simulation of a real product or
process, that uses the best available physical models,
sensors, and data updates to mirror the life of its physical
counterpart.
*A digital twin is used to simulate the behaviour of a real
manufacturing process, assisted by real-time monitoring and
feedback.
*Real-world data is transmitted into the modeled system to
complete simulations, validate the system, and dynamically
adjust it where needed.
*This really separates a digital, synchronizing twin with
conventional simulated systems that do not provide real-time
monitoring and feedback to the field.
128.
129. *Digital twins have high synchronization and fidelity
with the physical space, full data interaction and
convergence and self-evolution. Full data interaction and
convergence is the ability to connect data generated in
various physical and virtual spaces with each other, not
only from the manufacturing floor, but also the
combination of historical and real time data. Self-
evolution stands for a digital twins ability to update data
in real time, so that the virtual model is continuously
improving by comparison between real and virtual.
130. *As the assembly process is mapped out in the digital
twin, you can model product functionality and simulate
the expected performance at the various assembly stages
using the product simulation model. This can be used to
develop in-line tests that ensure the product is
performing as expected – to weed out early failures,
mitigate yield losses and reduce scrap costs. The digital
twin provides insights on the operation of the physical
process without having to actually execute it.
131. *Using digital twin based simulations, the design process
in the manufacturing industry can be iteratively
optimized and personalized. Digital twins can be used to
quickly test new product functions, behavior, structures
and manufacturability. Defects can be found easier,
which speeds up verification and testing processes.
Furthermore, the whole manufacturing process can be
managed easier.
132.
133. *Before committing to the actual manufacturing process,
digital twins can be used to virtually assess operators,
materials, tools and the working environment. Through
simulation, manufacturing strategies and planning can
be made before production starts. This way, the
simulated environment brings together the data from
multiple aspects of the product lifecycle, stimulating
better product design and the traceability of quality. It
makes it easier to synergize between stages of the
lifecycle, since these can be tested before
implementation. Thus, using digital twins can make
product development shorter, more effective, and can
avoid possible errors.
134. *
*Increasingly humans and robots collaborate to
perform even more irregular or complicated tasks.
*Cobots aim to merge robotic capabilities, such as
high levels of accuracy, speed and repeatability, with
the flexibility and cognitive skills of human workers
by sharing each other's workspaces.
*An overarching aim of Industrie 4.0 is higher
efficiency and productivity through automation,
thereby reducing costs. In this context, the role of
factory workers and operators will be changing from
laborious processes to knowledge intensive one.
135.
136. *
*To take full advantage of human skills, it is important
that intuitive user interfaces are properly designed, so
that human operators can easily program and interact
with robots. Specifically, simplified ways to interact
with industrial robots in a reduced time, while
minimizing user’s errors and preserving situational
awareness, are needed. A lot of manufacturers are
looking for a robotic system that they can teach like an
intern: Show once, then perform. By giving corrective
feedback and by showing what is important, the required
action is defined.
137.
138. *
*Most new approaches often promise more intuitive control
and command, allowing less-skilled workers to use robotic
assistance. The goal here is to reduce the cognitive
requirements put upon the operator, such that the robots can
remain accessible to users of all skill levels. The approaches
taken to design new user interfaces can mostly be classified
in online programming, like traditional lead-through
programming, relying on the use of a teach pendant (a
control box for programming the motions of a robot) to
move the robot through its required motion cycle, or walk-
through programming, allowing the user to physically move
the end of the robot through its desired positions. The robot
is able to record and reproduce the trajectory afterwards.
139. *
*Intuitive interfaces can be designed in a human-
friendly manner through speech based
commands, gesture-based commands, eye
tracking, facial expression and haptics; replacing
the traditional keyboard to computer relation
between human and machine. These
developments are sometimes gathered under the
term Tactile Internet, describing an environment
to control the IoT in real-time using tactile and
haptic sensations.
140.
141. *
*A lot of interest for human-machine interfaces (HMI) has gone
towards augmented reality (AR) and virtual reality (VR). These
techniques can increase productivity while also enhancing (the
feeling of) safety for operators. They are further supported by
simulated environments, like the ones created by digital twin
technology, and complement humans' innate ability to act and
interact with physical objects in a space.
*The most promising use cases of AR and VR are thus also related
to design, assembly, maintenance, or training. For example during
assembly extra information can be displayed in the operators field
of vision using AR. Another example is remote maintenance, an
application of AR that is gaining momentum. It supports the
technician in maintaining or repairing the machinery from a
remote location, thus reducing travel for the engineer of the
machine vendor and allowing high level workers to work at
multiple locations.
142.
143. *Models for humans in the
production line
*When looking at the future, there are three possible models for
humans in production lines:
*The process worker concerned with monitoring the mainly
automated production process. With limited responsibility this is
the end of a development that would eliminate humans from the
production line;
*The role model. Here, the human is a template for mechanical
skills that need to be taught to machines. In this scenario the
human is skilled and has a diverse array of tasks, working to
better their skills outside of the production process, to be
transferred later to robots to gain competitive advantages.
*The architect. The factory worker is the bridge between
production using cyber physical systems, connecting customers,
developers, and production lines.
144. *The roles humans can take in the factory of
the future will require a different set of
skills, and most certainly require new or
renewed qualifications.
*The potential impact of this on our future
of work is expected to impact not only
manufacturers but also our economies and
societies as a whole.