The document describes an intelligent tool called the Analyser for Robust Design that uses data analysis to optimize industrial processes and ensure zero-defect products. It has modular components that can analyze curve data, identify relationships between process parameters and quality requirements, and monitor key metrics. The tool replaces manual analysis, reduces failure rates, and captures expert knowledge to continuously improve processes and products.
The document describes the verification and validation process for algorithms used in allocation and production ordering. It involves:
1) Building a model of the current and new algorithms to simulate their performance under different input conditions.
2) Verifying the new algorithm is constructed correctly by comparing its output to expected output.
3) Validating the new algorithm meets business objectives by evaluating its performance against production and stakeholder criteria.
4) Reviewing the algorithm verification and validation methodology.
The document summarizes CATIA product lifecycle management solutions, including:
1) Supporting all stages of product development from concept to manufacturing with integrated CAD, PDM, CAM, and analysis tools.
2) Providing a single, associative 3D model environment to reduce costs and iterations throughout the process.
3) Enabling collaboration, knowledge sharing, and process automation across the product lifecycle.
Introduction to HyperWorks for linear static and non linear quasi static anal...Altair
The suite HyperWorks (HW) was introduced in a course of master degree in Mechanical Engineering. The course titled “Design of Production Processes” has the aim to develop a market-pull product in a set of steps beginning with a perception of a market opportunity and ending in production, sale and delivery activities. HW was used for linear static and non linear quasi static analyses to correctly size the different parts which make up the investigated product. The lessons were organized starting from a general overview on Finite Element Analyses, followed by an introduction to HW interface and, finally, the steps necessary to set-up and to analyze the results of different types of simulation were described. Furthermore, topology optimization of particular sub-components were performed by Inspire.
Speakers
Francesco Gagliardi, University of Calabria
This document discusses process planning. It defines process planning as systematically determining how a product will be manufactured economically. The objectives are to prepare instructions for manufacturing a product and its parts along with specifications. Process planning activities include analyzing part requirements, determining operation sequences, selecting equipment, calculating times, and documenting plans. Common approaches are manual and computer-aided process planning (CAPP), which can be retrieval-based or generative.
Justyna Rybicka discuss using WITNESS software to model support decision maki...Lanner
Presenting at the Lanner predictive simulation conference, 2016, Justyna Rybicka from Cranfield University explores the use of Lanner simulation software WITNESS to model support decision making tool for flexible manufacturing system optimisation.
DFMEA is a method used to identify potential risks in new designs. It involves identifying failure modes, their causes and effects, and assigning severity, occurrence, and detection rankings. High risk issues are addressed. 3D modeling and technical drawings are used to visualize designs. Bills of materials list components. Benchmarking evaluates competitors. Geometric dimensioning specifies tolerances. Simulations and calculations analyze performance. Design reviews check for issues. Value analysis reduces costs. Waste elimination aims to remove non-value-adding activities.
Presentation on the promises and pitfalls of applying Agile in a Quality Management System. How do you get the benefits of agile while maintaining quality and regulatory compliance?
StarCom Information Technology is a public company in India that aims to be a global provider of business intelligence, analytics, and big data solutions. It has formed a strategic partnership with a global analytics company to distribute their products in the Asia Pacific region. SigmaPlot is a graphing and data analysis software that helps scientists and researchers visualize and analyze data through various statistical tests and graph types. It provides an optimized interface and additional features in version 13 like forest plots, kernel density plots, and dot density graphs.
The document describes the verification and validation process for algorithms used in allocation and production ordering. It involves:
1) Building a model of the current and new algorithms to simulate their performance under different input conditions.
2) Verifying the new algorithm is constructed correctly by comparing its output to expected output.
3) Validating the new algorithm meets business objectives by evaluating its performance against production and stakeholder criteria.
4) Reviewing the algorithm verification and validation methodology.
The document summarizes CATIA product lifecycle management solutions, including:
1) Supporting all stages of product development from concept to manufacturing with integrated CAD, PDM, CAM, and analysis tools.
2) Providing a single, associative 3D model environment to reduce costs and iterations throughout the process.
3) Enabling collaboration, knowledge sharing, and process automation across the product lifecycle.
Introduction to HyperWorks for linear static and non linear quasi static anal...Altair
The suite HyperWorks (HW) was introduced in a course of master degree in Mechanical Engineering. The course titled “Design of Production Processes” has the aim to develop a market-pull product in a set of steps beginning with a perception of a market opportunity and ending in production, sale and delivery activities. HW was used for linear static and non linear quasi static analyses to correctly size the different parts which make up the investigated product. The lessons were organized starting from a general overview on Finite Element Analyses, followed by an introduction to HW interface and, finally, the steps necessary to set-up and to analyze the results of different types of simulation were described. Furthermore, topology optimization of particular sub-components were performed by Inspire.
Speakers
Francesco Gagliardi, University of Calabria
This document discusses process planning. It defines process planning as systematically determining how a product will be manufactured economically. The objectives are to prepare instructions for manufacturing a product and its parts along with specifications. Process planning activities include analyzing part requirements, determining operation sequences, selecting equipment, calculating times, and documenting plans. Common approaches are manual and computer-aided process planning (CAPP), which can be retrieval-based or generative.
Justyna Rybicka discuss using WITNESS software to model support decision maki...Lanner
Presenting at the Lanner predictive simulation conference, 2016, Justyna Rybicka from Cranfield University explores the use of Lanner simulation software WITNESS to model support decision making tool for flexible manufacturing system optimisation.
DFMEA is a method used to identify potential risks in new designs. It involves identifying failure modes, their causes and effects, and assigning severity, occurrence, and detection rankings. High risk issues are addressed. 3D modeling and technical drawings are used to visualize designs. Bills of materials list components. Benchmarking evaluates competitors. Geometric dimensioning specifies tolerances. Simulations and calculations analyze performance. Design reviews check for issues. Value analysis reduces costs. Waste elimination aims to remove non-value-adding activities.
Presentation on the promises and pitfalls of applying Agile in a Quality Management System. How do you get the benefits of agile while maintaining quality and regulatory compliance?
StarCom Information Technology is a public company in India that aims to be a global provider of business intelligence, analytics, and big data solutions. It has formed a strategic partnership with a global analytics company to distribute their products in the Asia Pacific region. SigmaPlot is a graphing and data analysis software that helps scientists and researchers visualize and analyze data through various statistical tests and graph types. It provides an optimized interface and additional features in version 13 like forest plots, kernel density plots, and dot density graphs.
The document discusses different measurement technologies that can meet the increasing inspection requirements of high-production turning equipment. Non-contact turned part measuring centers like the Tesascan can automatically inspect small dental implants. Vision systems provide significant throughput advantages over manual inspection for parts like piston valves. Touch probe systems allow inspection of turned and milled contours directly on the machine for the highest throughput.
A Computer Vision Application for In Vitro Diagnostics DevicesAdaCore
Software for Computer Vision is often used in embedded systems to support automatic workflow and to providesafety critical functionality.
This presentation will show how one of the world-leading companies for in vitro diagnostic (IVD) solutions, bioMérieux, applied Computer Vision in laboratory instruments and transitioned from using black-box and integrated devices to designing more distributed hardware/software solutions.
Additionally the presentation will provide a case study on how Altran succeeded in defining the tool chain underpinning the entire development, from Computer Vision algorithms design to embedded firmware deployment and testing.
Architectural flexibility and a robust design control approach were fundamental drivers for the project and will facilitate meeting safety and regulatory requirements in bioMérieux’s diagnostics solutions.
towards a model-based framework for development of engineering1 (1)Jinzhi Lu
This document proposes a model-based framework for developing engineering tool-chains that support cyber-physical systems modeling and simulation. It presents the SPIT framework, which takes a systems approach to support MBSE tool-chain development. The framework addresses functionalities of MBSE tool-chains from a systems engineering perspective. Demo tool-chains are developed to support co-simulation of CPS using MBSE. Future work includes extending tool integration languages to formalize co-simulation tool-chains and analyzing the functional dynamics of MBSE enterprise transitioning.
The document outlines the objectives and key concepts covered in Chapter 14 of the textbook "Accounting Information Systems, 6th edition". The objectives include the in-house development phase of the SDLC, tools used such as CASE and PERT/Gantt charts, structured vs object-oriented design approaches, documentation types, and the commercial software option. It then covers the phases of SDLC in more detail including in-house development, commercial packages, and maintenance. Design approaches like structured and object-oriented are defined. Documentation, testing, training and post-implementation review are discussed as part of system delivery.
Saving resources with simulation webinar 092011Scott Althouse
IBM Rational Rhapsody provides solutions to help reduce costs and risks when developing complex products and systems. It allows for early validation and verification of designs through model-based simulation and testing. This helps find defects earlier in the development process when they are cheaper to fix. Rational Rhapsody also improves collaboration, requirements management, and automation of testing.
Engineering plant facilities 17 artificial intelligence algorithms & proc...Luis Cabrera
The document discusses how artificial intelligence algorithms and machine learning can be applied to optimize manufacturing operations management. It provides examples of how production planning and scheduling algorithms could use data on customer orders, production line status, and inventory to dynamically assign products to production lines in an optimal sequence. The algorithms aim to minimize production time and maximize value based on game theory strategies. Machine learning and AI dashboards could help automate 84% of managerial tasks related to coordination and control, improving efficiency.
An Integrated Simulation Tool Framework for Process Data ManagementCognizant
Digital simulations play an increasing role in product lifecycle management (PLM) processes and simulation data management (SDM) based on the PLM XML protocol, which is a key interface with computer-aided engineering (CAE) applications. We offer a framework for aligning SDM with the overall product development process to shorten lead times and optimize output.
Business Process Analytics: From Insights to PredictionsMarlon Dumas
Keynote talk at the 13th Baltic Conference on Databases and Information Systems, Trakai, Lithuania, 2 July 2018.
Abstract
Business process analytics is a body of methods for analyzing data generated by the execution of business processes in order to extract insights about weaknesses and improvement opportunities, both at the tactical and operational levels. Tactical process analytics methods (also known as process mining) allow us to understand how a given business process is actually executed, if and how its execution deviates with respect to expected or normative pathways, and what factors contribute to poor process performance or undesirable outcomes. Meantime, operational process analytics methods allow us to monitor ongoing executions of a business process in order to predict future states and undesirable outcomes at runtime (predictive process monitoring). Existing methods in this space allow us to predict, for example, which task will be executed next in a case, when, and who will perform it? When will an ongoing case complete? What will its outcome be and how can negative outcomes be avoided? This keynote will present a framework for conceptualizing business process analytics methods and applications. The talk will provide an overview of state-of-art methods and tools in the field and will outline open challenges and research opportunities.
Model-Based Risk Assessment in Multi-Disciplinary Systems EngineeringEmanuel Mätzler
This document proposes a model-based approach for risk assessment in multi-disciplinary engineering projects. It involves defining metamodels for production system models, link models between artifacts, and metrics. Metrics are defined using the Structured Metrics Metamodel and calculated by executing queries on the system models. Measurement results are stored in the metrics model. The approach aims to support risk assessment across distributed, versioned engineering artifacts represented in AutomationML. Future work includes expanding the metrics, integrating dynamic aspects, and visualizing results.
A CASE Lab Report - Project File on "ATM - Banking System"joyousbharat
A CASE Lab Report - Project File on "ATM - Banking System"
The software to be designed will control a simulated automated teller machine
(ATM) having a magnetic stripe reader for reading an ATM card, a keyboard and
display for interaction with the customer, a slot for depositing envelopes, a
dispenser for cash (in multiples of $20), a printer for printing customer receipts, and
a key-operated switch to allow an operator to start or stop the machine. The ATM
will communicate with the bank's computer over an appropriate communication
link. (The software on the latter is not part of the requirements for this problem.)
Choosing the right process improvement tool for your project.
Learn how an experienced engineer decides when simulation is the right tool for his projects,
and when it isn't.
With the evolution of process improvement software, it can be difficult to decide the right tool for the job. Using something too powerful and complex can be a lengthy and unnecessary process, but underestimating the depth of analysis required and choosing something too simplistic early in a project can result in repeated work later.
Virtual Commissioning of Small to Medium Scale Industry Using the Concepts of...IJERA Editor
Small scale industries produce certain products depending on the type of industry they have established. If these small scale industries decide to become medium scale certain changes have to be incorporated in plant layout to meet certain requirements. Certain changes include change in layout design, introducing new machines and equipments in the industry in order to produce new component .To implement these changes in the company we have to get information regarding the new component the company would produce based on this information we have design new plant layout. The purpose of this project is to plan a suitable plant layout which could meet company requirement. To design a new plant layout we are using Delmia as the simulation software. DELMIA Production System Simulation allows the process planner to validate the manufacturing system dynamically. Product flow and operation time, as well as scheduled maintenance and random equipment failure events, are simulated to help the planner understand how they will impact the system’s capacity. Process planners can determine if changes to the system are needed to achieve the desired production demands.
Augury: Real-Time Insights for the Industrial IoTScyllaDB
Augury stores and serves time-series features from massive streams of IoT data, both for real-time insights, and offline learning and analytics. Learn about Augury’s needs and constraints, their solution evaluation and architecture, and fundamental practices for efficient data modeling, plus get a glimpse into the next-gen architecture at Augury, with a view on time-series feature storage and serving.
Introduction to mechanical engineering design & manufacturing withAkshit Rajput
The document provides an introduction to mechanical engineering design and manufacturing using Fusion 360. It discusses key aspects of mechanical engineering design including the design process, digital manufacturing, CAD/CAM/CAE software such as Fusion 360, and CNC machining. Some key points covered include the steps in the engineering design process, advantages of digital manufacturing, differences between CAD, CAM, and CAE tools, and differences between numeric control and computer numeric control systems.
The document provides an overview of computer-aided design (CAD) and computer-aided manufacturing (CAM). It discusses the reasons for implementing CAD, including increasing productivity and improving quality. It describes the basic CAD modeling techniques of wireframe, solid modeling, and engineering analysis tools. The document also outlines common CAM applications in manufacturing planning, such as computer-aided process planning and computer-assisted NC part programming. Applications in manufacturing control discussed include process monitoring, quality control, and just-in-time production systems.
Applying linear regression and predictive analyticsMariaDB plc
In this session Alejandro Infanzon, Solutions Engineer, introduces the linear regression and statistical functions that debuted in MariaDB ColumnStore 1.2, and how you can use them to support powerful analytics. He explains how to perform even-more-powerful analytics by writing multi-parameter user-defined functions (UDFs) – also new in MariaDB ColumnStore 1.2.
The document discusses five core quality tools: APQP (Advanced Product Quality Planning), FMEA (Failure Modes and Effects Analysis), PPAP (Production Part Approval Process), MSA (Measurement Systems Analysis), and SPC (Statistical Process Control). It provides a brief overview of each tool, noting that APQP is used to develop products that satisfy customers, FMEA ensures potential problems are considered, PPAP ensures products meet specifications, MSA assesses measurement systems, and SPC enables process control and improvement. The document emphasizes that these five tools are considered core tools for quality management.
The document discusses different measurement technologies that can meet the increasing inspection requirements of high-production turning equipment. Non-contact turned part measuring centers like the Tesascan can automatically inspect small dental implants. Vision systems provide significant throughput advantages over manual inspection for parts like piston valves. Touch probe systems allow inspection of turned and milled contours directly on the machine for the highest throughput.
A Computer Vision Application for In Vitro Diagnostics DevicesAdaCore
Software for Computer Vision is often used in embedded systems to support automatic workflow and to providesafety critical functionality.
This presentation will show how one of the world-leading companies for in vitro diagnostic (IVD) solutions, bioMérieux, applied Computer Vision in laboratory instruments and transitioned from using black-box and integrated devices to designing more distributed hardware/software solutions.
Additionally the presentation will provide a case study on how Altran succeeded in defining the tool chain underpinning the entire development, from Computer Vision algorithms design to embedded firmware deployment and testing.
Architectural flexibility and a robust design control approach were fundamental drivers for the project and will facilitate meeting safety and regulatory requirements in bioMérieux’s diagnostics solutions.
towards a model-based framework for development of engineering1 (1)Jinzhi Lu
This document proposes a model-based framework for developing engineering tool-chains that support cyber-physical systems modeling and simulation. It presents the SPIT framework, which takes a systems approach to support MBSE tool-chain development. The framework addresses functionalities of MBSE tool-chains from a systems engineering perspective. Demo tool-chains are developed to support co-simulation of CPS using MBSE. Future work includes extending tool integration languages to formalize co-simulation tool-chains and analyzing the functional dynamics of MBSE enterprise transitioning.
The document outlines the objectives and key concepts covered in Chapter 14 of the textbook "Accounting Information Systems, 6th edition". The objectives include the in-house development phase of the SDLC, tools used such as CASE and PERT/Gantt charts, structured vs object-oriented design approaches, documentation types, and the commercial software option. It then covers the phases of SDLC in more detail including in-house development, commercial packages, and maintenance. Design approaches like structured and object-oriented are defined. Documentation, testing, training and post-implementation review are discussed as part of system delivery.
Saving resources with simulation webinar 092011Scott Althouse
IBM Rational Rhapsody provides solutions to help reduce costs and risks when developing complex products and systems. It allows for early validation and verification of designs through model-based simulation and testing. This helps find defects earlier in the development process when they are cheaper to fix. Rational Rhapsody also improves collaboration, requirements management, and automation of testing.
Engineering plant facilities 17 artificial intelligence algorithms & proc...Luis Cabrera
The document discusses how artificial intelligence algorithms and machine learning can be applied to optimize manufacturing operations management. It provides examples of how production planning and scheduling algorithms could use data on customer orders, production line status, and inventory to dynamically assign products to production lines in an optimal sequence. The algorithms aim to minimize production time and maximize value based on game theory strategies. Machine learning and AI dashboards could help automate 84% of managerial tasks related to coordination and control, improving efficiency.
An Integrated Simulation Tool Framework for Process Data ManagementCognizant
Digital simulations play an increasing role in product lifecycle management (PLM) processes and simulation data management (SDM) based on the PLM XML protocol, which is a key interface with computer-aided engineering (CAE) applications. We offer a framework for aligning SDM with the overall product development process to shorten lead times and optimize output.
Business Process Analytics: From Insights to PredictionsMarlon Dumas
Keynote talk at the 13th Baltic Conference on Databases and Information Systems, Trakai, Lithuania, 2 July 2018.
Abstract
Business process analytics is a body of methods for analyzing data generated by the execution of business processes in order to extract insights about weaknesses and improvement opportunities, both at the tactical and operational levels. Tactical process analytics methods (also known as process mining) allow us to understand how a given business process is actually executed, if and how its execution deviates with respect to expected or normative pathways, and what factors contribute to poor process performance or undesirable outcomes. Meantime, operational process analytics methods allow us to monitor ongoing executions of a business process in order to predict future states and undesirable outcomes at runtime (predictive process monitoring). Existing methods in this space allow us to predict, for example, which task will be executed next in a case, when, and who will perform it? When will an ongoing case complete? What will its outcome be and how can negative outcomes be avoided? This keynote will present a framework for conceptualizing business process analytics methods and applications. The talk will provide an overview of state-of-art methods and tools in the field and will outline open challenges and research opportunities.
Model-Based Risk Assessment in Multi-Disciplinary Systems EngineeringEmanuel Mätzler
This document proposes a model-based approach for risk assessment in multi-disciplinary engineering projects. It involves defining metamodels for production system models, link models between artifacts, and metrics. Metrics are defined using the Structured Metrics Metamodel and calculated by executing queries on the system models. Measurement results are stored in the metrics model. The approach aims to support risk assessment across distributed, versioned engineering artifacts represented in AutomationML. Future work includes expanding the metrics, integrating dynamic aspects, and visualizing results.
A CASE Lab Report - Project File on "ATM - Banking System"joyousbharat
A CASE Lab Report - Project File on "ATM - Banking System"
The software to be designed will control a simulated automated teller machine
(ATM) having a magnetic stripe reader for reading an ATM card, a keyboard and
display for interaction with the customer, a slot for depositing envelopes, a
dispenser for cash (in multiples of $20), a printer for printing customer receipts, and
a key-operated switch to allow an operator to start or stop the machine. The ATM
will communicate with the bank's computer over an appropriate communication
link. (The software on the latter is not part of the requirements for this problem.)
Choosing the right process improvement tool for your project.
Learn how an experienced engineer decides when simulation is the right tool for his projects,
and when it isn't.
With the evolution of process improvement software, it can be difficult to decide the right tool for the job. Using something too powerful and complex can be a lengthy and unnecessary process, but underestimating the depth of analysis required and choosing something too simplistic early in a project can result in repeated work later.
Virtual Commissioning of Small to Medium Scale Industry Using the Concepts of...IJERA Editor
Small scale industries produce certain products depending on the type of industry they have established. If these small scale industries decide to become medium scale certain changes have to be incorporated in plant layout to meet certain requirements. Certain changes include change in layout design, introducing new machines and equipments in the industry in order to produce new component .To implement these changes in the company we have to get information regarding the new component the company would produce based on this information we have design new plant layout. The purpose of this project is to plan a suitable plant layout which could meet company requirement. To design a new plant layout we are using Delmia as the simulation software. DELMIA Production System Simulation allows the process planner to validate the manufacturing system dynamically. Product flow and operation time, as well as scheduled maintenance and random equipment failure events, are simulated to help the planner understand how they will impact the system’s capacity. Process planners can determine if changes to the system are needed to achieve the desired production demands.
Augury: Real-Time Insights for the Industrial IoTScyllaDB
Augury stores and serves time-series features from massive streams of IoT data, both for real-time insights, and offline learning and analytics. Learn about Augury’s needs and constraints, their solution evaluation and architecture, and fundamental practices for efficient data modeling, plus get a glimpse into the next-gen architecture at Augury, with a view on time-series feature storage and serving.
Introduction to mechanical engineering design & manufacturing withAkshit Rajput
The document provides an introduction to mechanical engineering design and manufacturing using Fusion 360. It discusses key aspects of mechanical engineering design including the design process, digital manufacturing, CAD/CAM/CAE software such as Fusion 360, and CNC machining. Some key points covered include the steps in the engineering design process, advantages of digital manufacturing, differences between CAD, CAM, and CAE tools, and differences between numeric control and computer numeric control systems.
The document provides an overview of computer-aided design (CAD) and computer-aided manufacturing (CAM). It discusses the reasons for implementing CAD, including increasing productivity and improving quality. It describes the basic CAD modeling techniques of wireframe, solid modeling, and engineering analysis tools. The document also outlines common CAM applications in manufacturing planning, such as computer-aided process planning and computer-assisted NC part programming. Applications in manufacturing control discussed include process monitoring, quality control, and just-in-time production systems.
Applying linear regression and predictive analyticsMariaDB plc
In this session Alejandro Infanzon, Solutions Engineer, introduces the linear regression and statistical functions that debuted in MariaDB ColumnStore 1.2, and how you can use them to support powerful analytics. He explains how to perform even-more-powerful analytics by writing multi-parameter user-defined functions (UDFs) – also new in MariaDB ColumnStore 1.2.
The document discusses five core quality tools: APQP (Advanced Product Quality Planning), FMEA (Failure Modes and Effects Analysis), PPAP (Production Part Approval Process), MSA (Measurement Systems Analysis), and SPC (Statistical Process Control). It provides a brief overview of each tool, noting that APQP is used to develop products that satisfy customers, FMEA ensures potential problems are considered, PPAP ensures products meet specifications, MSA assesses measurement systems, and SPC enables process control and improvement. The document emphasizes that these five tools are considered core tools for quality management.
Similar to Contech analyser for_robust_design_v1.6_en (20)
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
2. 2
Contents
Introduction: Analyser for Robust Design
The intelligent tool to analyse products and processes………….……………………............. 3
Modular structure and features........…………………………………………………….............. 4
Data analysis in the current Industry 4.0 environment..…………………………...….............. 5
Curve Analysis Module (KA)
Specifications and features: Curve Analysis Module............................................................. 6
Operating principle: Curve Analysis Module……………………………………....................... 7
User interface and teach in process………………………………………………………………. 8
Application possibilities: Curve Analysis Module..……..............…....................................... 10
Transfer Function Module (TF)
Specifications and features: Transfer Function Module………………………............…….... 11
Operating principle: Transfer Function Module..………………………………..............…..... 12
Application possibilities: Transfer Function Module…......……………………...........….........13
SPC Module (SPC)
Specifications and features: SPC Module.…..……………..................………............……....14
Operating principle: SPC Module.................…..………………………………..............…..... 15
Analyser for Robust Design
Your benefits using the Analyser for Robust Design............................................................ 16
Contact us……...........................................................….…………….................................. 17
3. 3
for Robust Design
The intelligent tool to analyse products and processes
Although for many companies it is still a ‘Vision for Industry 4.0’, we already have a standard
product available:
The Analyser for Robust Design,
the fully automated online tool for zero-defect and robust products and processes.
» Distinguishes OK and NOK cases based on curve characteristics and / or continuous
values (measurements) from sensor data
» Provides the connections between failure types and their root causes and offers
troubleshooting actions and solutions for each failure type
» Provides cause-effect interactions between requirements Yi (outputs) and cause
variables / influencing factors Xi (inputs)
» Predicts the achievement of requirements Yi
» Helps optimizing the process parameters and influencing factors for each requirement
» Real-time processing of small series of measurements and big data collections
» Supports clearing developments, accelerates industrialisation of products and
processes and provides features to ensure proper series production processes and
production process analysis
» Decreases failure rates consistently, reduces rework and the risk of warranty claims
» Provides a significant contribution to a zero-defect strategy and process optimisation
with a digital integration of the production in terms of Industry 4.0
4. 4
for Robust Design
Modular structure and features
The Analyser for Robust Design has an application specific, modular structure with
standardised modules:
5. for Robust Design
Data analysis in the current Industry 4.0 environment
In the Industry 4.0 environment, there are currently a few data analysis approaches for
product- / process data and measurable requirements.
Data analysis in the current Industry 4.0 environment and pioneering role of the Analyser for Robust Design
With the pioneering role and the modular structure, the Analyser for Robust Design
provides several application possibilities for prescriptive statistics (‘What should be
done?’). In this block, only very little and often no human interaction and statistical
knowledge is necessary to analyse data and decide on troubleshooting actions.
5
6. Curve Analysis Module
Specifications and features
Curve Analysis Module (KA):
» Knowledge based, intelligent expert system in production and assembly
» Distinguishes OK and NOK cases based on curve characteristics and / or continuous
values (measurements) from sensor data
» Identifies typical failure types from NOK product validation and process curve
characteristics or NOK measurements and displays them with their percentage
» Connects the failure types to their cause(s), provides troubleshooting actions and
solutions and generates a knowledge database in your company
Statistical curve and failure type analysis with the Analyser for Robust Design
6
7. 7
Curve Analysis Module
Operating principle with teach in process using expert knowledge
Production and assembly processes
Digital monitoring and storage of process parameters and their
(curve) characteristics.
Data interface, user interface
Flexible data interface to import (curve) data (online or via
database). Graphical user interface to display individual
parameters / curve characteristics.
Teach in process
One-time storage of curve specific expert knowledge
for different failure types or other irregularities.
Book of Knowledge
Stored failure causes, recommended troubleshooting actions and
solution proposals to fix the failures (optional).
Failure cause analysis
Automated analysis of all process output data and identification of
the occurred failure types. Graphic presentation of the results with
failure type percentages and prioritised causes and troubleshooting
actions / solutions (optional via Book of Knowledge).
8. 8
Curve Analysis Module
User interface and teach in process
The process curves that were selected for analysis with the Analyser for Robust Design
are displayed in the user interface with their respective parameters, process limits and further
information. In a one-time teach in process, an expert can evaluate a representative part of
the curves and store the respective failure types in the system.
User interface with exemplary process curve (here: NOK curve) in teach in mode
With the stored knowledgebase, the Analyser for Robust Design with Curve Analysis
Module can provide the most probable root causes for the failure types identified from the
curve characteristics and prioritise them. The root causes are in turn linked to the respective
troubleshooting actions and solutions.
9. The Curve Analysis Module of the Analyser for Robust Design is able to provide
systematic analysis of failure types and causes and return the respective troubleshooting
actions and solutions based on the so called ‚Book of Knowledge‘, a technology database
with expert knowledge.
9
Failure types (with percentages) and link to prioritized failure causes and troubleshooting actions /
solutions in the ‘Book of Knowledge’
10. 10
Curve Analysis Module
Application possibilities
» Fundamental idea: use of curve characteristics for failure analysis
» Universal operating principle with a great variety of individual, cross-sector
application possibilities
» All digitally monitored product and process parameters represented by their curve
characteristics and / or continuous values can be analysed, controlled and optimised with
the Curve Analysis Module
» Preventive failure avoidance or reactive product and process optimisation
Possibleapplicationareas (examples)
1. Digitally monitored fastenings (and similar assembly processes)
> Torque level [Nm] by torsion angle [°]
> Different tightening strategies, several stages / phases
2. Hysteresis loops
> Materials engineering: stress-strain diagrams
> Valves: Force [N] by travel [mm] at certain waypoints and Fmax.
> Control engineering
3. Acoustics and vibration issues
> Vibrations, NVH issues (sound pressure level [dB] by number of rotations [rpm])
> Acoustics and noise optimisation
4. Injection molding (e.g. of plastics)
> Pressure [Pa, bar, psi] by time [s] or travel [mm]
> Temperature [°C, °F] by time [s] or travel [mm]
> Optimisation of the open / closed loop control technology
and many more…
11. 11
Transfer Function Module
Specifications and features
Transfer Function Module (TF):
» Filters the relevant cause variables influencing the quality of a product, process or
process step
» Identifies and describes the dependencies and cause-effect interactions between
product / process requirements (Yi) and the respective process parameters and cause
variables (Xi) with statistical models represented as transfer functions:
The Xi depicts 1 to n measurable, monitored cause variables or parameters that influence the resulting
quality requirements (Yi) of the product or process. f depicts the transfer function.
» Predicts the conformance to requirements (Yi) for the used process parameters and
cause variables Xi
» Optimises the process parameters and cause variables based on the cause-effect
interactions.
Filtering of statistically significant cause variables Xi
12. 12
Transfer Function Module
Operating principle for product / process optimisation
Production and assembly processes
Digital monitoring and storage of measurable product or
process parameters.
Teach in – cause variables
Compilation and prioritisation of the statistically significant and
measurable cause variables (Vital View).
Teach-In - Transfer function
Identification of the transfer function(s) and cause-effect
interactions for products or (sub)processes using statistical data
analysis methods. Storage in the technology database
‘Book of Knowledge for Transfer Functions’.
Qualitymanagement and product / process optimisation
Optimised parametrisation and tolerance management of products
and processes by continuous and predictive calculations based on
the transfer function(s).
13. 13
Transfer Function Module
Application possibilities
» Fundamental idea: use of statistical models based on measurable cause variables for
product and process optimisation
» Universal operating principle for a great variety of individual, cross-sector application
possibilities
» All monitored product and process parameters can be predicted, controlled and
optimised using the Transfer Function Module
» Preventive and reactive product and process optimisation
Possibleapplicationareas (examples)
1. Monitored adhesive bonding processes (and similar assembly processes)
> Adhesion, cohesion and peel strength = f (viscosity, temperature, adhesive bead size, etc.)
2. Hysteresis loops
> Breaking systems: response time = f (Xi)
> Valves: recuperating force = f (Xi)
3. Acoustics and vibration issues
> Vibrations, NVH issues: sound pressure level [dB] by number of rotations [rpm] = f (Xi)
> Acoustics and noise optimisation
4. Injection molding (e.g. of plastics)
> Shrinkage, corrugation, dimensions = f(pressure [Pa, bar, psi], holding pressure time [s],
tool temperature [°C, °F], etc.)
5. Extrusion of plastics / rubber
> Profile geometry, hardness, stress-strain coefficient = f (Xi)
and many more…
14. 14
SPC Module
Specifications and features
SPC Module (SPC):
» Processes product and process parameters that are monitored online and are available as
curve characteristics and / or continuous values
» Monitors defined and selected SPC parameters continuously and fully automated
including an early warning system for deviations
» Provides stored reaction plans with tasks per parameter and requirement violation,
including responsibilities, troubleshooting actions and solutions
Graphical definition of a SPC parameter in a series of curves
15. 15
SPC Module
Operating principle for product / process monitoring
Production and assembly processes
Digital online monitoring and storage of measurable
product or process parameters.
Teach in – SPC parameters
Graphical or analogue definition of statistically significant and
measurable SPC parameters.
Monitoring of product / process parameters
Continuous and fully automated monitoring of the defined
SPC parameters including an early warning system for
deviations and slide control to set constant product and
process parameters.
Qualitymanagement and reaction plans
Reaction plans with tasks per parameter and requirement violation
including responsibilities, troubleshooting actions and solutions.
16. 16
for Robust Design
Your benefits using the Analyser for Robust Design
Replaces existing manual or automated methods for failure analysis, product &
process monitoring and optimisation
Increases the efficiency and effectivity of data analysis
Identifies failure types and root causes fast and provides troubleshooting actions
and solutions
Creates robust products and processes
Decreases failure rates consistently, reduces rework and the risk of warranty
claims
Identifies cause-effect interactions between requirements and cause variables and
predicts the conformance to requirements
Monitors defined and selected SPC parameters continuously and fully automated
including an early warning system and reaction plans for deviations
Stores retrievable expert knowledge and / or transfer functions for products and
processes in the technology database ‘Book of Knowledge (for Transfer
Functions)’
Provides a universal operating principle for a great variety of individual,
cross-sector application possibilities
Includes a standardised interface to import sensor data
Only system requirement: Windows 7 or newer
17. Do you have any questions or do you need any further information?
We are looking forward to support you concerning your issues and projects.
Please do not hesitate to contact us or visit our homepages:
Consulting and engineering services: www.mts-contech.de
Analyser for Robust Design: www.contech-analyser.de
Postal address:
Wernher-von-Braun-Straße 8
D-82256 Fürstenfeldbruck
Office:
Oskar-von-Miller-Straße 4d
D-82256 Fürstenfeldbruck
Telephone +49.8141.888 403-0
Fax +49.3222.376 25 38
E-mail info@mts-contech.com
www.mts-contech.de
www.contech-analyser.de