The document outlines an agenda for a workshop on digital twin technology from a manufacturing quality perspective. The workshop will discuss using CAE simulation and artificial intelligence to create digital twins for closed-loop in-process quality improvement. Speakers will discuss applications in automotive and aerospace manufacturing and how digital twin technology can shorten lead times and ramp-up times by improving quality.
Overview of the Exascale Additive Manufacturing Projectinside-BigData.com
In this video from the HPC User Forum in Santa Fe, John Turner from ORNL presents: Overview of the Exascale Additive Manufacturing Project.
"Fully exploiting future exascale architectures will require a rethinking of the algorithms used in the large scale applications that advance many science areas vital to DOE and NNSA, such as global climate modeling, turbulent combustion in internal combustion engines, nuclear reactor modeling, additive manufacturing, subsurface flow, and national security applications. The newly established Center for Efficient Exascale Discretizations (CEED) in DOE’s Exascale Computing Project (ECP) aims to help these DOE/NNSA applications to take full advantage of exascale hardware by using state-of-the-art ‘high-order discretizations’ that provide an order of magnitude performance improvement over traditional methods."
Watch the video: http://wp.me/p3RLHQ-gHb
Learn more: https://exascaleproject.org/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
#SiriusCon 2015: Talk by Christophe Boudjennah "Experimenting the Open Source...Obeo
Capella is a Model Based Systems Engineering (MBSE) solution using Sirius for its diagrams rendering.
It has been initially developed in house by Thales and has been open sourced (in Polarsys) within the context of the CLARITY project. This was actually the very first step of CLARITY, which aims at developing and structuring an international ecosystem around Capella. The CLARITY project now investigates customization capabilities for Capella and aims at complementing the ecosystem with a community that brings together major actors of the entire engineering value chain (industrials, integrators, technology providers and consultants, academia) for open innovation in MBSE within Capella.
In this context, Areva and Airbus Defence & Space already made lots of experimentations and are helping the ecosystem to mature up by providing feedbacks to the community. In this talk, you will get an overview of what those 2 Industrial companies have realized so far.
[About Christophe Boudjennah:
Christophe is a senior system/software architect and project manager. His experience leads him to work for various domains such as defense, IT, or the Automotive industry. Most of his career has been focused on Systems Engineering for complex embedded systems, whether it is from the "methods and tools provider" point of view or from the operational one. He is now working for Obeo, and is dealing with various open source and systems engineering related topics. One of his current main responsibilities is to be the project coordinator of Clarity, a large R&D project whose purpose is to open-source Capella (an industrial workbench for system engineering).]
Peter Zimm - MRO WORKSHOP - SPOTLIGHT: Additive manufacturing (3D printing) is expected to have a profound impact on global supply chains, including in the aviation industry. What does 3D printing mean for the future of manufacturers and MROs?
Overview of the Exascale Additive Manufacturing Projectinside-BigData.com
In this video from the HPC User Forum in Santa Fe, John Turner from ORNL presents: Overview of the Exascale Additive Manufacturing Project.
"Fully exploiting future exascale architectures will require a rethinking of the algorithms used in the large scale applications that advance many science areas vital to DOE and NNSA, such as global climate modeling, turbulent combustion in internal combustion engines, nuclear reactor modeling, additive manufacturing, subsurface flow, and national security applications. The newly established Center for Efficient Exascale Discretizations (CEED) in DOE’s Exascale Computing Project (ECP) aims to help these DOE/NNSA applications to take full advantage of exascale hardware by using state-of-the-art ‘high-order discretizations’ that provide an order of magnitude performance improvement over traditional methods."
Watch the video: http://wp.me/p3RLHQ-gHb
Learn more: https://exascaleproject.org/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
#SiriusCon 2015: Talk by Christophe Boudjennah "Experimenting the Open Source...Obeo
Capella is a Model Based Systems Engineering (MBSE) solution using Sirius for its diagrams rendering.
It has been initially developed in house by Thales and has been open sourced (in Polarsys) within the context of the CLARITY project. This was actually the very first step of CLARITY, which aims at developing and structuring an international ecosystem around Capella. The CLARITY project now investigates customization capabilities for Capella and aims at complementing the ecosystem with a community that brings together major actors of the entire engineering value chain (industrials, integrators, technology providers and consultants, academia) for open innovation in MBSE within Capella.
In this context, Areva and Airbus Defence & Space already made lots of experimentations and are helping the ecosystem to mature up by providing feedbacks to the community. In this talk, you will get an overview of what those 2 Industrial companies have realized so far.
[About Christophe Boudjennah:
Christophe is a senior system/software architect and project manager. His experience leads him to work for various domains such as defense, IT, or the Automotive industry. Most of his career has been focused on Systems Engineering for complex embedded systems, whether it is from the "methods and tools provider" point of view or from the operational one. He is now working for Obeo, and is dealing with various open source and systems engineering related topics. One of his current main responsibilities is to be the project coordinator of Clarity, a large R&D project whose purpose is to open-source Capella (an industrial workbench for system engineering).]
Peter Zimm - MRO WORKSHOP - SPOTLIGHT: Additive manufacturing (3D printing) is expected to have a profound impact on global supply chains, including in the aviation industry. What does 3D printing mean for the future of manufacturers and MROs?
[Capella Days 2020] MBSE and the High-Tech Equipment Industry, how do they ma...Obeo
by Teun Hendriks, Senior Research Fellow (TNO-ESI)
MBSE is by now widely adopted in the Aerospace and Defense industry. These industries however typically develop their systems with very large, one-of-a-kind system projects, following the V model. A large upfront (MB)SE effort is justified then as the cost of late failures are extremely high.
The High-Tech Equipment Industry on the other hand develops their systems incrementally with an agile Systems Engineering process, supporting many product variants and often a configure-to-order sales process.
How does MBSE match up with the characteristics of the High-Tech Equipment Industry? ESI and its partners have started a collaborative project to study together whether, and if so how, MBSE, or MBSE elements, can improve Systems Engineering in this industry business context.
This talk will provide an update on the state of SE in the High-Tech Equipment Industry, its use of models, and the outlook on the fit of MBSE in this industry context.
Case-study by CT-Ingénierie: Capella in the preliminary design of the micro l...Obeo
Discover why Capella has been chosen by CT Ingenierie and its partners, and how it has been deployed for guaranteeing the correct coordination between teams, requirement following, and for a rigorous description of the sub-systems developed by their partners.
CT Ingenierie is involved in the ENVOL project (European Newspace Vertical Orbital Launcher). Developing a small launch vehicle enabling cheap, frequent, and flexible access to the Low Earth Orbit.
What is project ENVOL?
The EU-funded ENVOL project intends to establish the first European commercial, competitive and green launch service.
The project will designate an innovative and industrial low-cost launch system, then demonstrate and advance crucial launcher technologies to guarantee market preparedness and competitiveness, then prepare a business plan and identify the institutions able to attract investments.
This presentation sums up the needs and the business benefits for realizing traceability in simulation-based engineering and testing of autonomous vehicles (Level 4 / 5), explaining the first versions of elaborated data and process standards, demonstrate first implementations and gives an outlook of the next steps.
Traceability for Verification and Validation in Autonomous DrivingSteven Vettermann
On April 29th the Software Demonstrator TRACY for realizing traceability with the two publicly funded projects SET Level and V&V Methods is presented. Traceability is key for assuring functional safety in simulation-based engineering and testing. But when realizing traceability, additional benefits can be offered to user for easing their daily business, assuring quality and compliance etc.
GE Inspection Technologies reviews case studies of industrial production process control in the castings, aerospace and automotive industries using advanced computed tomography CT techniques. Presented to the American Society of Nondestructive Testing (ASNT) at the 2014 Annual Conference
System of Systems modeling comes with a tough decision for practitioners using traditional SysML V1 tools. Do I go with SysML V1, or do I look at Unified Architecture Framework? Capella eliminates that challenge with one notation that can be used for both.
By Tony Komar (Siemens)
Tony Komar has been practicing and supporting systems engineering for over 35 years.
Today he is a key contributor to the development and deployment of model-based system engineering products for Siemens Digital Industries Software.
[Capella Days 2020] Integrating MBSE and Life Cycle Assessment for Removing P...Obeo
by Arnaud Dieumegard and Raphaël Pagé (Obeo)
More than 8 million tons of plastic are dumped in the oceans every year. If we don’t take action, in 2050, there will be more plastic than fishes in the oceans!
The SeaCleaners association has been founded to provide global, long-term and worldwide solutions for fighting against ocean plastic pollution. The flagship project initiated by The SeaCleaners is the MANTA, the first seagoing vessel capable of collecting and processing in continuous flow large quantities of macro plastic waste floating at the surface of oceans.Its design has been focusing on many innovative technologies in the field of renewable energy production or by limiting her global carbon footprint maximizing the energy self-sufficiency.
In this talk we will present a Capella extension developped by Obeo in partnership with The SeaCleaners and Altran to facilitate the Life-Cycle Assessment (LCA) of complex systems. This extension consists of additional concepts added to Capella for inventorying components' physical characteristics and the substances they consume and/or emit. This information attached to the system architecture can be automatically exported as an initial inventory analysis to LCA tools used by environmental experts to perform their impact analysis (such as SimaPro and OpenLCA).
Experimented on a MANTA subsystem, we will show how this integration between Capella and LCA Tools accelerates the evaluation of the impacts a system has on its environment, and helps system designers to make architecture decisions that are better for the planet.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/improving-nursing-care-with-privacy-sensitive-edge-computer-vision-a-presentation-from-kepler-vision-technologies/
Harro Stokman, Chief Executive Officer and Founder of Kepler Vision Technologies, presents the “Improving Nursing Care with Privacy-Sensitive Edge Computer Vision” tutorial at the May 2021 Embedded Vision Summit.
Around the world, there is a serious and growing shortage of nurses. Nursing care at night is a particular challenge because night shifts are less attractive to nurses and since patients’ needed rest can be disturbed by in-person monitoring. Computer-vision-based activity detection provides the ability to reliably monitor patients and alert nurses when assistance is needed.
But creating and deploying a solution requires overcoming several significant obstacles. For example, a single overhead camera with a fisheye lens capable of viewing an entire room delivers very distorted images. In addition, privacy is a critical concern. And, care facilities often have minimal IT infrastructure and staff. In this talk, Dr. Harro Stokman explains how his company’s Kepler Night Nurse product has overcome these challenges and achieved registration as a medical device.
The existence of countless proprietary file formats and the exchange of 3D CAD data has been a significant problem since the beginning of 3D CAD modeling. CAD applications and methods using digital data are constantly changing, which predicates the need for a solution to share validated and accurately translated data, thus the birth of STEP242
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.
Service Virtualization: What, Who, When, and HowTechWell
Service virtualization provides many benefits for both development and test teams. For testers, service virtualization empowers them to work in parallel with their development counterparts and take control of their own schedules. They no longer have to wait for development to complete their work or to get access to a restricted system such as a mainframe or a third party API. Test teams can get the basic details from dev and/or use a sample request and response pair to create a virtual service themselves. With no need to wait on others to start testing, testing can start at iteration one freeing up more time for exploratory, integration, and performance testing. With service virtualization, developers spend less time creating mocks and stubs, and more time developing and completing unit tests. The virtual services they create can be shared for additional testing, ultimately saving everybody time and effort. Join Kenneth Merkel and take the first step in adopting virtual services by learning more about what you can virtualize, how the services get created, common use cases, and adoption benefits.
[Capella Days 2020] MBSE and the High-Tech Equipment Industry, how do they ma...Obeo
by Teun Hendriks, Senior Research Fellow (TNO-ESI)
MBSE is by now widely adopted in the Aerospace and Defense industry. These industries however typically develop their systems with very large, one-of-a-kind system projects, following the V model. A large upfront (MB)SE effort is justified then as the cost of late failures are extremely high.
The High-Tech Equipment Industry on the other hand develops their systems incrementally with an agile Systems Engineering process, supporting many product variants and often a configure-to-order sales process.
How does MBSE match up with the characteristics of the High-Tech Equipment Industry? ESI and its partners have started a collaborative project to study together whether, and if so how, MBSE, or MBSE elements, can improve Systems Engineering in this industry business context.
This talk will provide an update on the state of SE in the High-Tech Equipment Industry, its use of models, and the outlook on the fit of MBSE in this industry context.
Case-study by CT-Ingénierie: Capella in the preliminary design of the micro l...Obeo
Discover why Capella has been chosen by CT Ingenierie and its partners, and how it has been deployed for guaranteeing the correct coordination between teams, requirement following, and for a rigorous description of the sub-systems developed by their partners.
CT Ingenierie is involved in the ENVOL project (European Newspace Vertical Orbital Launcher). Developing a small launch vehicle enabling cheap, frequent, and flexible access to the Low Earth Orbit.
What is project ENVOL?
The EU-funded ENVOL project intends to establish the first European commercial, competitive and green launch service.
The project will designate an innovative and industrial low-cost launch system, then demonstrate and advance crucial launcher technologies to guarantee market preparedness and competitiveness, then prepare a business plan and identify the institutions able to attract investments.
This presentation sums up the needs and the business benefits for realizing traceability in simulation-based engineering and testing of autonomous vehicles (Level 4 / 5), explaining the first versions of elaborated data and process standards, demonstrate first implementations and gives an outlook of the next steps.
Traceability for Verification and Validation in Autonomous DrivingSteven Vettermann
On April 29th the Software Demonstrator TRACY for realizing traceability with the two publicly funded projects SET Level and V&V Methods is presented. Traceability is key for assuring functional safety in simulation-based engineering and testing. But when realizing traceability, additional benefits can be offered to user for easing their daily business, assuring quality and compliance etc.
GE Inspection Technologies reviews case studies of industrial production process control in the castings, aerospace and automotive industries using advanced computed tomography CT techniques. Presented to the American Society of Nondestructive Testing (ASNT) at the 2014 Annual Conference
System of Systems modeling comes with a tough decision for practitioners using traditional SysML V1 tools. Do I go with SysML V1, or do I look at Unified Architecture Framework? Capella eliminates that challenge with one notation that can be used for both.
By Tony Komar (Siemens)
Tony Komar has been practicing and supporting systems engineering for over 35 years.
Today he is a key contributor to the development and deployment of model-based system engineering products for Siemens Digital Industries Software.
[Capella Days 2020] Integrating MBSE and Life Cycle Assessment for Removing P...Obeo
by Arnaud Dieumegard and Raphaël Pagé (Obeo)
More than 8 million tons of plastic are dumped in the oceans every year. If we don’t take action, in 2050, there will be more plastic than fishes in the oceans!
The SeaCleaners association has been founded to provide global, long-term and worldwide solutions for fighting against ocean plastic pollution. The flagship project initiated by The SeaCleaners is the MANTA, the first seagoing vessel capable of collecting and processing in continuous flow large quantities of macro plastic waste floating at the surface of oceans.Its design has been focusing on many innovative technologies in the field of renewable energy production or by limiting her global carbon footprint maximizing the energy self-sufficiency.
In this talk we will present a Capella extension developped by Obeo in partnership with The SeaCleaners and Altran to facilitate the Life-Cycle Assessment (LCA) of complex systems. This extension consists of additional concepts added to Capella for inventorying components' physical characteristics and the substances they consume and/or emit. This information attached to the system architecture can be automatically exported as an initial inventory analysis to LCA tools used by environmental experts to perform their impact analysis (such as SimaPro and OpenLCA).
Experimented on a MANTA subsystem, we will show how this integration between Capella and LCA Tools accelerates the evaluation of the impacts a system has on its environment, and helps system designers to make architecture decisions that are better for the planet.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/improving-nursing-care-with-privacy-sensitive-edge-computer-vision-a-presentation-from-kepler-vision-technologies/
Harro Stokman, Chief Executive Officer and Founder of Kepler Vision Technologies, presents the “Improving Nursing Care with Privacy-Sensitive Edge Computer Vision” tutorial at the May 2021 Embedded Vision Summit.
Around the world, there is a serious and growing shortage of nurses. Nursing care at night is a particular challenge because night shifts are less attractive to nurses and since patients’ needed rest can be disturbed by in-person monitoring. Computer-vision-based activity detection provides the ability to reliably monitor patients and alert nurses when assistance is needed.
But creating and deploying a solution requires overcoming several significant obstacles. For example, a single overhead camera with a fisheye lens capable of viewing an entire room delivers very distorted images. In addition, privacy is a critical concern. And, care facilities often have minimal IT infrastructure and staff. In this talk, Dr. Harro Stokman explains how his company’s Kepler Night Nurse product has overcome these challenges and achieved registration as a medical device.
The existence of countless proprietary file formats and the exchange of 3D CAD data has been a significant problem since the beginning of 3D CAD modeling. CAD applications and methods using digital data are constantly changing, which predicates the need for a solution to share validated and accurately translated data, thus the birth of STEP242
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.
Service Virtualization: What, Who, When, and HowTechWell
Service virtualization provides many benefits for both development and test teams. For testers, service virtualization empowers them to work in parallel with their development counterparts and take control of their own schedules. They no longer have to wait for development to complete their work or to get access to a restricted system such as a mainframe or a third party API. Test teams can get the basic details from dev and/or use a sample request and response pair to create a virtual service themselves. With no need to wait on others to start testing, testing can start at iteration one freeing up more time for exploratory, integration, and performance testing. With service virtualization, developers spend less time creating mocks and stubs, and more time developing and completing unit tests. The virtual services they create can be shared for additional testing, ultimately saving everybody time and effort. Join Kenneth Merkel and take the first step in adopting virtual services by learning more about what you can virtualize, how the services get created, common use cases, and adoption benefits.
Gearing Up for the 21st Century Revolution:
Industrial enterprises around the world are retooling their factories with advanced technologies to boost manufacturing flexibility and speed, achieving new levels of overall equipment effectiveness (OEE), supply chain responsiveness, and customer satisfaction in the process. This renaissance reflects very real pressures industry players face today.
IRJET-To Implement Cloud Computing by using Agile Methodology in Indian E-Gov...IRJET Journal
P.V.S.S.Gangadhar, A.K.Shrivastava, Ragini Shukla "To Implement Cloud Computing by using Agile Methodology in Indian E-Governance ",International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Many developed countries in the world, uses Information and communication technology to deliver public services in a more efficient & easy way. The benefits of the Electronic Governance are huge and day by day increasing from one to many public services. But implementation of the governance is constrained due to 1) high costs of investment 2) shortage of domain experts 3) diverse and irreconcilable systems and 4) Security and privacy issues. With the Invent & rise of Cloud computing, i am looking various aspects of use of cloud computing in e-governance is emerged. Cloud computing can solve many of the above mentioned hurdles and provide better way to e-gov expansion, but it has some risks also. Agile development treats optimize the chance provided by cloud computing by doing software relinquishes iteratively and getting end user feedback more frequently and quickly.
To enable industrial companies to identify startups that can emerge as capable technology partners for the long term, we have compiled a catalogue of Industrial Deep Tech startups from the Forge Portfolio, to serve as a valuable resource.
Startups in the Forge Portfolio categorised under Industrial Deep Tech address manufacturing as a sector - apart from other core industrial sectors such as power, energy, resources, transportation, logistics, defence, aerospace, space etc. These startups are broadly categorised under Digital Technology (DT) and Operations Technology (OT).
Digital Technology addresses process innovations augmenting capabilities in the areas of digitisation, digitalisation, automation (robotics), analytics, autonomy, and intelligence. Operations Technology relates to innovations in the areas of design (product engineering services), materials (carbon composites), production & processing (additive manufacturing suppliers for new end product categories that traditional fabrication factories don't serve), and business models (manufacturing aggregator platform etc.).
This catalogue of 20 Industrial Tech startups is further organized into 7 Industrial Digital Transformation Themes which are further split into 11 sub-themes - that broadly outline operational capabilities, emerging/futuristic technology domains, or product categories in specific market segments that help organise and prioritise the various opportunities for value creation by industrial companies.
Cisco has developed a comprehensive approach, the Mass Scale Networking (MSN) Transformation Journey, that covers both aspects. On the technology front, technologies such as Segment Routing, EVPN, orchestration, automation, HW/SW disaggregation are covered. On the operating model side, the use of advanced APIs, model driven operations, Infrastructure as Code (IaC), and others are also covered. The primary objective of this session being to create a methodical and structured approach to drive an SP’s MSN Journey.
Made Smarter Innovation: Sustainable Smart Factory Competition BriefingKTN
This competition briefing outlines how this funding opportunity aims to support industrial research that addresses digital innovations to improve the sustainability of manufacturing processes.
An Engineering Digital Twin to Accelerate Time to Productionaseptingfilling
✓Understanding the design and its
interaction with the environment
✓Support dimensioning
✓Early exploration of limits and testing in
the virtual world
✓Support definition of good real-world tests
✓Support communication within the team
and to a wide range of stakeholders
Internet of Things - structured approach to the physical plant network - Rock...Carotek
The convergence of new technologies that securely connect plant information with enterprise systems can bring greater productivity, better utilization of assets, and improved decision-making to industrial companies. By bridging the gap between factory-level systems and enterprise systems, Rockwell Automation and Cisco can show how the connected enterprise offers ease of use, lower total cost of ownership, and improved operations.
Ben Peace, Knowledge Transfer Manager - Sustainable Manufacture, at the KTN presented on the funding opportunities available through Innovate UK and the Knowledge Transfer Network
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Using IESVE for Room Loads Analysis - Australia & New Zealand
Deep learning enhanced digital twin for Closed-loop In-Process Quality Improvement
1. Presented by: Darek Ceglarek
WMG, University of Warwick, Coventry, UK
Authors: Pasquale Franciosa, Sumit Sinha, Manoj Babu, Emile Glorieux, Darek Ceglarek
4th February 2020 | Professor Lord Bhattacharya Building (NAIC), WMG | University of Warwick, Coventry, CV4 7AL
DIGITAL TWIN TECHNOLOGY
- A MANUFACTURING QUALITY PERSPECTIVE -
Get Connected: When CAE Simulation Meets Artificial Intelligence
Deep Learning Enhanced Digital Twin for Closed-loop In-Process Quality Improvement
2. REGISTRATION AND REFRESHMENTS
• Exhibition, demonstrations and engineering games
Welcome and opening remarks
• Dr David Bott, Principal Fellow, WMG
Digital twin technology – a manufacturing perspective
• Professor Darek Ceglarek, WMG
Academic and industry research collaboration – the Mathworks suite
• Bulat Khusainov, Technical Specialist, Mathworks
BREAK AND REFRESHMENTS
• Exhibition, demonstrations and engineering games
From concept to production implementation – the Remote Laser
Welding (RLW) process for aluminium door assembly
• Marcus Henry, Structures Research Manager, Jaguar Land Rover
• Sujit Chatterjee, Lead Engineer, AME, Jaguar Land Rover
LUNCH AND NETWORKING
• Exhibition, demonstrations and engineering games
09:30
10:00
10:15
10:45
11:15
11:30
12:00
Mathworks CAE simulation suite – case in point from
automotive and aerospace
• Bulat Khusainov, Technical Specialist, Mathworks
‘Variation Response Method’ CAE simulation suite – What is it?
• Dr Pasquale Franciosa, Associate Professor, WMG
BREAK AND REFRESHMENTS
• Exhibition, demonstrations and engineering games
‘Variation Response Method’ CAE simulation suite – Generating
training data for deep learning
• Dr Pasquale Franciosa, Associate Professor, WMG
Deep learning in manufacturing – predicting and preventing
manufacturing defects
• Sumit Sinha, PhD student, WMG
Wrap up and close
• Professor Darek Ceglarek, WMG
• Dr David Bott, Principal Fellow, WMG
END
13:00
13:45
14:15
14:30
15:30
16:15
16:30
A G E N D A
Get Connected: When CAE Simulation Meets Artificial Intelligence
4th February 2020 | Professor Lord Bhattacharya Building (NAIC), WMG | University of Warwick, Coventry, CV4 7AL
TECHNICAL SESSIONS
3. WMG @ University of Warwick, United Kingdom
WMG – an independent interdisciplinary academic department equivalent to Industrial & Systems Engg.
o An academic department of the University of Warwick
o Employing over 600 staff
o Working across research and education centres on the
Warwick campus
o Delivering education programmes in seven countries, and
collaborating globally on research and development
o An annual programme of £200m (industrial & in-kind
support)
o Strong relationships with over 1,000 global companies,
and supporting 1,800 SMEs
o Part of the HVM Catapult network HVM Catapult - Supports RTD &
technology maturation from concept
(TRL 3-4) to pre-production (TRL 6-7)
4. WMG @ University of Warwick, United Kingdom
New Development - ‘National Automotive Innovative Campus’ (NAIC)
• £150m investment - the biggest industrial & private sector investment in any UK university
(2017)
• A 33,000m2 collaborative research environment
• Unique infrastructure & national focus for R&D addressing UK Automotive Council agenda
• Creating 1,000 & attracting 3,000 further R&D jobs
• Bringing global R&D of major Tier 1 suppliers to co-locate
Supporting RTD and technology maturation from concept (TRL 3-4) to pre-
production (TRL 6-7)
5. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
6. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
GOAL
Towards Near-Zero Defects
Challenges:
Cell design with capabilities to simulate
‘production parts’
Quality Improvement with capabilities for
quality defects Root Cause Analysis (RCA)
Quality Defects
Data
Manufacturing
Closed-loop in-process (CLIP) quality improvement
CAE Simulations
Variation Response Method (VRM)
Artificial Intelligence
Deep learning for Manufacturing
Takes as input a set of
control parameters
Gives as output a
system response
Constraints such as
specification limits
System
Control parameters
have to be estimated
Given the output is
known
System
Constraints such as
specification limits
Design Simulation tools
(1) To support system optimisation
(2) To improve process capability
(3) Variation reduction
Quality Improvement tools
(1) To support system optimisation
(2) Intelligent root cause analysis of defects
(3) Preventive control actions
7. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
What we do? Framework
Digital Twin for rapid scaling-up & deployment of new technology
AI tools
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
Quality in
closed-loop
Monitoring
in closed-loop
Smart Connected FactoryProduct & Process
Design
Products in Field
Goal:
Towards Zero-defects
Objective 2: Real-time optimization of
quality & operations performance
Objective 1: ‘Right First Time’
Towards Resilient System
Simulations
in closed-loop
Enterprise Analytics
In-use dataQuality Data
2
3
1
Device analytics
In-line monitoring
Digital
Lifecycle
Management
Multi-disciplinary
Defect Simulator
System
Configurator
RCA / CAPA
8. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
• Configuration, optimization & control of assembly process with
deformable parts from design-to-production .
• Digital twin with embedded CAE simulations
• Multi-disciplinary optimization of system performance & configuration
Simulation
in closed-loop1
Monitoring
in closed-loop2
Quality
in closed-loop3
What we do? Framework
Digital Twin for rapid scaling-up & deployment of new technology
AI tools
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
Quality in
closed-loop
Monitoring
in closed-loop
Smart Connected FactoryProduct & Process
Design
Products in Field
Goal:
Towards Zero-defects
Objective 2: Real-time optimization of
quality & operations performance
Objective 1: ‘Right First Time’
Towards Resilient System
Simulations
in closed-loop
Enterprise Analytics
In-use dataQuality Data
2
3
1
Device analytics
In-line monitoring
Digital
Lifecycle
Management
Multi-disciplinary
Defect Simulator
System
Configurator
RCA / CAPA
9. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
• In-line & In-process quality monitoring to accelerate RCA –incl. off-line
& in-line programming of robotic 3D scanner;
• In-use data from intelligent assets & 3rd party devices.
• Statistical Quality Control for Point-Clouds & big data.
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
• Configuration, optimization & control of assembly process with
deformable parts from design-to-production .
• Digital twin with embedded CAE simulations
• Multi-disciplinary optimization of system performance & configuration
Simulation
in closed-loop1
Monitoring
in closed-loop2
Quality
in closed-loop3
What we do? Framework
Digital Twin for rapid scaling-up & deployment of new technology
AI tools
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
Quality in
closed-loop
Monitoring
in closed-loop
Smart Connected FactoryProduct & Process
Design
Products in Field
Goal:
Towards Zero-defects
Objective 2: Real-time optimization of
quality & operations performance
Objective 1: ‘Right First Time’
Towards Resilient System
Simulations
in closed-loop
Enterprise Analytics
In-use dataQuality Data
2
3
1
Device analytics
In-line monitoring
Digital
Lifecycle
Management
Multi-disciplinary
Defect Simulator
System
Configurator
RCA / CAPA
10. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
• In-line & In-process quality monitoring to accelerate RCA –incl. off-line
& in-line programming of robotic 3D scanner;
• In-use data from intelligent assets & 3rd party devices.
• Statistical Quality Control for Point-Clouds & big data.
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
• Data-driven Root Cause Analysis (RCA) of quality (6-sigma & No-Fault-
Found) defects using in-line and in-process data.
• RCA-driven Corrective & Preventive Actions (CAPA).
• CAPA-driven continuous improvement.
• Multi-physics simulator of manufacturing defects.
• Configuration, optimization & control of assembly process with
deformable parts from design-to-production .
• Digital twin with embedded CAE simulations
• Multi-disciplinary optimization of system performance & configuration
Simulation
in closed-loop1
Monitoring
in closed-loop2
Quality
in closed-loop3
What we do? Framework
Digital Twin for rapid scaling-up & deployment of new technology
AI tools
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
Quality in
closed-loop
Monitoring
in closed-loop
Smart Connected FactoryProduct & Process
Design
Products in Field
Goal:
Towards Zero-defects
Objective 2: Real-time optimization of
quality & operations performance
Objective 1: ‘Right First Time’
Towards Resilient System
Simulations
in closed-loop
Enterprise Analytics
In-use dataQuality Data
2
3
1
Device analytics
In-line monitoring
Digital
Lifecycle
Management
Multi-disciplinary
Defect Simulator
System
Configurator
RCA / CAPA
11. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
• In-line & In-process quality monitoring to accelerate RCA –incl. off-line
& in-line programming of robotic 3D scanner;
• In-use data from intelligent assets & 3rd party devices.
• Statistical Quality Control for Point-Clouds & big data.
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
• Data-driven Root Cause Analysis (RCA) of quality (6-sigma & No-Fault-
Found) defects using in-line and in-process data.
• RCA-driven Corrective & Preventive Actions (CAPA).
• CAPA-driven continuous improvement.
• Multi-physics simulator of manufacturing defects.
• Configuration, optimization & control of assembly process with
deformable parts from design-to-production .
• Digital twin with embedded CAE simulations
• Multi-disciplinary optimization of system performance & configuration
Simulation
in closed-loop1
Monitoring
in closed-loop2
Quality
in closed-loop3
What we do? Examples
Rapid scaling-up & deployment of new technology
CAE Simulators embedded
into digital twin & VR
ConvolutionalNeuralNets
Reinforcementlearning
AI tools
Fuzzy models
Spatio-temporalmodels
Genetics Algorithms
Polynomial Chaos
Rule-based systems
Digital twin
& VR (TRL 3-4)
Lab demo
(TRL 6)
Pre-production
pilot trials
(TRL7)
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
Scale-up simulator &
In-process/inline monitoring
RCA and CAPA
simulator and controller
12. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
• In-line & In-process quality monitoring to accelerate RCA –incl. off-line
& in-line programming of robotic 3D scanner;
• In-use data from intelligent assets & 3rd party devices.
• Statistical Quality Control for Point-Clouds & big data.
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
• Data-driven Root Cause Analysis (RCA) of quality (6-sigma & No-Fault-
Found) defects using in-line and in-process data.
• RCA-driven Corrective & Preventive Actions (CAPA).
• CAPA-driven continuous improvement.
• Multi-physics simulator of manufacturing defects.
• Configuration, optimization & control of assembly process with
deformable parts from design-to-production .
• Digital twin with embedded CAE simulations
• Multi-disciplinary optimization of system performance & configuration
Simulation
in closed-loop1
Monitoring
in closed-loop2
Quality
in closed-loop3
What we do? Examples
Laser WeldingRobotic
AI for rapid scaling-up & deployment of new technology
Fixture
CAE Simulators embedded into digital twin & VR
AI tools
Genetics Algorithms
Polynomial Chaos
Rule-based systems
Digital twin
& VR (TRL 3-4)
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
Lab demo
(TRL 6)
Pre-production
pilot trials
(TRL7)
Scale-up simulator &
In-process/inline monitoring
RCA and CAPA
simulator and controller
13. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
• In-line & In-process quality monitoring to accelerate RCA –incl. off-line
& in-line programming of robotic 3D scanner;
• In-use data from intelligent assets & 3rd party devices.
• Statistical Quality Control for Point-Clouds & big data.
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
• Data-driven Root Cause Analysis (RCA) of quality (6-sigma & No-Fault-
Found) defects using in-line and in-process data.
• RCA-driven Corrective & Preventive Actions (CAPA).
• CAPA-driven continuous improvement.
• Multi-physics simulator of manufacturing defects.
• Configuration, optimization & control of assembly process with
deformable parts from design-to-production .
• Digital twin with embedded CAE simulations
• Multi-disciplinary optimization of system performance & configuration
Simulation
in closed-loop1
Monitoring
in closed-loop2
Quality
in closed-loop3
What we do? Examples
Laser WeldingRobotic
AI for rapid scaling-up & deployment of new technology
Fixture
CAE Simulators embedded into digital twin & VR
In-lineIn-processScale-up Sim
Scale-up simulator & In-process/inline monitoring
AI tools
Fuzzy models
Spatio-temporalmodels
Genetics Algorithms
Polynomial Chaos
Rule-based systems
Digital twin
& VR (TRL 3-4)
Lab demonstrator (TRL6)
System tested on ~80 parts
DEEP LEARNING ENHANCED DIGITAL TWIN FOR
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
Pre-production
pilot trials
(TRL7)
RCA and CAPA
simulator and controller
14. DIGITAL LIFECYCLE
MANAGEMENT (DLM)
For more information: Prof. Darek Ceglarek; Prof. Pasquale Franciosa, {d.j.Ceglarek, p.Franciosa}@warwick.ac.uk
• In-line & In-process quality monitoring to accelerate RCA –incl. off-line
& in-line programming of robotic 3D scanner;
• In-use data from intelligent assets & 3rd party devices.
• Statistical Quality Control for Point-Clouds & big data.
DLM research & technology development provides a transformative framework integrating Industry 4.0 enablers such as big data, cloud computing, robotics and AI:
- To create precise digital twins with capabilities to integrate data from interconnected systems with multi-disciplinary simulation of products & processes.
- To achieve resilient performance in production systems such as predict behaviour of process machinery to either optimise existing equipment for increased productivity & quality; or
introduce new equipment into an assembly line with minimum disruption.
Business value
• Shorten lead time by.
• Increasing Right-First-Time
• Rapid deployment of new process
technologies.
• Reduction of installation and commissioning
time & cost.
• Shorten ramp-up time by.
• Shortening Mean-Time-to-Detection (MTTD)
of quality defects
• Shortening Mean-Time-to-Resolution
(MTTR) ) of quality defects
• Improved quality (6-sigma)
• Applications: automotive,
aerospace, consumer goods &
others.
• Data-driven Root Cause Analysis (RCA) of quality (6-sigma & No-Fault-
Found) defects using in-line and in-process data.
• RCA-driven Corrective & Preventive Actions (CAPA).
• CAPA-driven continuous improvement.
• Multi-physics simulator of manufacturing defects.
• Configuration, optimization & control of assembly process with
deformable parts from design-to-production .
• Digital twin with embedded CAE simulations
• Multi-disciplinary optimization of system performance & configuration
Simulation
in closed-loop1
Monitoring
in closed-loop2
Quality
in closed-loop3
ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN MANUFACTURING:
CLOSED-LOOP IN-PROCESS QUALITY CONTROL & IMPROVEMENT
What we do? Examples
Laser WeldingRobotic
AI for rapid scaling-up & deployment of new technology
Fixture
CAE Simulators embedded into digital twin & VR
In-lineIn-processScale-up Sim
Scale-up simulator & In-process/inline monitoring
CAPARCAFault ID
RCA and CAPA simulator and controller
ConvolutionalNeuralNets
Reinforcementlearning
2nd shift1st shiftLaunchPre-Launch
6-sigmaQuality
New production Launch
AI tools
Fuzzy models
Spatio-temporalmodels
Genetics Algorithms
Polynomial Chaos
Rule-based systems
2018 Innovista Award from
JLR for the Most
Innovative Piloted
Technology
Digital twin
& VR (TRL 3-4)
Lab demonstrator (TRL6)
System tested on ~80 parts
Pre-production pilot trials (TRL7)
System tested on ~3000 parts
15. Laser Welding
process parameters
Off-line
programming of
robotic joining and
handling
Fixture/jigs
Simulations
in closed-loop1
Determining optimum fixture design for a batch of non-ideal parts
Video explaining design optimization posted on the Youtube – RLW Navigator
Closed-loop In-Process Quality Improvement
Simulations in Closed-loop
16. Laser Welding
process parameters
Off-line
programming of
robotic joining and
handling
Fixture/jigs
Simulations
in closed-loop1
Gap= ~0.0 mm Gap= ~0.4 mm Gap= ~0.8 mm
AA5182; 1.5 mm to 2.2 mm; 4 m/min; 3.8kW (avg power)
Automatic process parameters selection for part-to-part gap bridging
Determine optimum laser parameter selection
What does it do?
(1) Definition of optimum process
parameters(i.e., power, speed), based
on defined output criteria (weld
quality, cycle time, etc.)
(2) Automatic identification of feasible
process
windows
Video explaining design optimization posted on the Youtube – RLW Navigator
Closed-loop In-Process Quality Improvement
Simulations in Closed-loop
17. Laser Welding
process parameters
Off-line
programming of
robotic joining and
handling
Fixture/jigs
Simulations
in closed-loop1
SCALABILITY: Capability for Scaling process parameters for varying welding speed
Gap= ~0.0 mm Gap= ~0.4 mm Gap= ~0.8 mm
AA5182; 1.5 mm to 2.2 mm; 4 m/min; 3.8kW (avg power)
ROBUSTNESS: Automatic process parameters selection for part-to-part
gap bridging
Closed-loop In-Process Quality Improvement
Simulations in Closed-loop
18. Industry challenges:
• Multi-disciplinary ‘simulation analytics’ to
configure, optimize & control assembly
process from design-to-production.
• Platform to integrate both product & process
models with measurement data.
• Applications: automotive, aerospace.
What we do:
• Configuration, optimization & control of assembly process
with deformable parts from design-to-production.
• In-line monitoring to accelerate root cause analysis (RCA).
• In-use data from intelligent assets & 3rd party devices.
• Data-driven Root Cause Analysis (RCA).
• RCA-driven Corrective & Preventive Actions (CAPA).
• CAPA-driven continuous improvement.
• Real-time optimization of machines, processes & plants.
• Machine learning for predictive capabilities.
Business Value:
• Improved interaction between
product and process design.
• Reduced engineering cost by
30%.
• Improved dimensional quality
20%.
• Reduction of installation,
commissioning and time to
launch.
• Rapid deployment of current or
new process (Remote Laser
Welding, RLW).
Current Industrial best-practice
‘Right-First-Time’ Design of RLW: Application to SUV door assembly
process - eliminated more than 100 engineering changes which
affected a total of 22 tooling elements or stiches.
Improvement obtained
using the developed “multi-
disciplinarydefect
simulator”
Laser Welding
process parameters
Off-line
programming of
robotic joining and
handling
Fixture/jigs
Simulations
in closed-loop1
Video explaining design optimization posted on the Youtube – RLW Navigator
Closed-loop In-Process Quality Improvement
Simulations in Closed-loop
19. Quality
in closed-loop3
What is it:
• No-Fault-Found (NFF), a term for when faults
are known to exist yet evade efforts to
identify them - a pervasive problem in high
tech
• Developed data analytics for NFF root cause
analysis called Fault Region Localization (FRL)
How is it unique:
• The developed NFF analytics for NFF root cause isolation
helped in elimination of a top-5 warranty problems in
one mobile phone OEM.
• The NFF analytics utilizes heterogeneous data: (i)
warranty & (ii) manufacturing to identify both candidate
KCs explaining the NFF and also determine their fault
and normal conditions windows .
Business Value:
• Airlines voted NFF "the most
important issue" by the annual
Avionics Maintenance Conference
in 2004. It continues to be among
the top-most items AMC discusses
each year, but there are no
systematic root cause analysis
approaches.
• Mobile phone: 1 in 7 phones are
returned by customers – 60% have
NFF (WDSGlobal Report, 2006).
Application:
No-fault found (NFF) root cause analysis at Motorola IDEN mobile phone
manufacturing :
NFFBattery
NFFAudio
Display
Poweroff
TxDefect
Warranty Faults
Importance
Assembly process
Top-5 warranty faults
IDEN mobile phobne
Results of the FRL Analytics
for NFF Battery failure
• Number of Variable: 170
• No. of Normal Sample: 1500 (as ‘*’)
• Faulty Samples: 20 (as ‘*’)
KC2:SpecsRegion
Key Characteristics (KC1): Specs Region
No-Fault-Fault
Region (NFFR)
Boundary
Region (BR)
Normal Region =
Specs Region – BR – NFFR
Closed-loop In-Process Quality Improvement
Quality in Closed-loop
20. OUR RTD JOURNEY
RTD JOURNEY, IMPACT AND ACKNOWLEDGMENTS
TRL 4-6
TRL 5-7-8
TRL 2-4
Demo+Pilot
RLW for Al
CAE Solutions:
In-process closed-loop
process quality control
TRL 2-4
CAE Solutions:
assembly simulations
(non-ideal compliant parts
Solutions+Demo
RLW for Steel
IR cell @Solihull (tests on ~3000 partsAR cell @WMG (tests on ~80 parts)
Up-scaling
In-line QC (GD&T) In-process QC (weld)
VR with embedded CAE simulation
21. Engineering Game
Illustration
New Applications of
3D Optical Scanner
-In-line Monitoring
BIW
RoofFrame w/ Cowl
Cowl LH Frame w/ cross MBRs Cowl LH
RR Cross MBRBIW Frame
RoofBowsLH Side Fr Comp RH Side FrameUB 3 Re-spot
SF Outer&Inner A-Pillar
SF Inner Assem
RR W/H Outer Fr Door Hinge SF Inner PanelThrough Side Fr. Outer
SF Outer Assem
T-Lamp Mount. SF Outer Sub
SF Outer Panel B-Pillar Inner RR Door Locker
Cowl Top UB Line 2
UB Line 2-0 Cowl Side
UB 1 Dash Fl Plenum
UB Line 1-1 Body Side Sills Dash Floor Plenum
RR Wheel House UB Line 1-0
RR Floor Ladder Front Rail Sub
Frt Floor Panel Dash Panel
Engine Compt 3 Frt W/H LH&RH
Engine Compt 2 Frt Low Frt Xmbr
Engine Compt 1 Frt Low RR Xmbr
Front Sill Hydroform Pnl
Multi-station Assembly Assembly stations
Laser Welding
process
parameters
Off-line
programming
of robots
Fixture/jigs
Fixture design for a batch of non-ideal parts
In-line 3D Optical Scanner Station
Metrology
Modelling
Robot
Simulation
1
2
1
,2,1,
,22221
,11211
1
2
1
mmmnmnnn
m
m
nn KCC
KCC
KCC
ccc
ccc
ccc
KPC
KPC
KPC
MatrixSOVA
Fixture
Modelling
Point-clouds data
Key Product Characteristics (KPCs)
Root Cause Analysis
Isolate Defective KCC(s)
Key Control Characteristics (KCCs)
KCC 2KCC 1
KCC 3
KCC 4
KCC 5
22. Engineering Game
Illustration
New Applications of
3D Optical Scanner
-In-line Monitoring
BIW
RoofFrame w/ Cowl
Cowl LH Frame w/ cross MBRs Cowl LH
RR Cross MBRBIW Frame
RoofBowsLH Side Fr Comp RH Side FrameUB 3 Re-spot
SF Outer&Inner A-Pillar
SF Inner Assem
RR W/H Outer Fr Door Hinge SF Inner PanelThrough Side Fr. Outer
SF Outer Assem
T-Lamp Mount. SF Outer Sub
SF Outer Panel B-Pillar Inner RR Door Locker
Cowl Top UB Line 2
UB Line 2-0 Cowl Side
UB 1 Dash Fl Plenum
UB Line 1-1 Body Side Sills Dash Floor Plenum
RR Wheel House UB Line 1-0
RR Floor Ladder Front Rail Sub
Frt Floor Panel Dash Panel
Engine Compt 3 Frt W/H LH&RH
Engine Compt 2 Frt Low Frt Xmbr
Engine Compt 1 Frt Low RR Xmbr
Front Sill Hydroform Pnl
Multi-station Assembly Assembly stations
Laser Welding
process
parameters
Off-line
programming
of robots
Fixture/jigs
Fixture design for a batch of non-ideal parts
In-line 3D Optical Scanner Station
Metrology
Modelling
Robot
Simulation
1
2
1
,2,1,
,22221
,11211
1
2
1
mmmnmnnn
m
m
nn KCC
KCC
KCC
ccc
ccc
ccc
KPC
KPC
KPC
MatrixSOVA
Fixture
Modelling
Point-clouds data
Key Product Characteristics (KPCs)
Root Cause Analysis
Isolate Defective KCC(s)
Key Control Characteristics (KCCs)
KCC 2KCC 1
KCC 3
KCC 4
KCC 5
CAESimulations
Variation Response Method (VRM)
Takes as input a set of
control parameters
Gives as output a
system response
Constraints such as
specification limits
System
Root Cause Analysis
Artificial Intelligence
Deep learning for Manufacturing
Control parameters
have to be estimated
Given the output is
known
System
Constraints such as
specification limits
23. Engineering Game
Life demo/competition
CAESimulations
Variation Response Method (VRM)
Takes as input a set of
control parameters
Gives as output a
system response
Constraints such as
specification limits
System
Root Cause Analysis
Artificial Intelligence
Deep learning for Manufacturing
Control parameters
have to be estimated
Given the output is
known
System
Constraints such as
specification limits
24. Name/Position Comments
Company/Division
Email/Telephone
DOES NOT EXIST
in my company
EXIST (not used)
in my company
INCREMENTAL
CHANGE
STEP
CHANGE
SIGNIFICANT
CHANGE
A. Presented software tools capabilities will provide for my company:
EXIST (limited use)
in my company
EXIST (common use)
in my company
(1) My company would be Interested
in the software tools as:
As ‘stand alone’ software
As ‘plug-in’ software
As ‘Integrated’ with software in my company
(2) Potential applications of interest:
• Automotive(please specify)
• Aerospace (please specify))
• Other (please specify)
Please mark in the table to the left your
response to the following two items:
A
Feedback
Simulation Tools: (1) Variation Response method (VRM); & (2) Deep learning in Manufacturing (DL in Mfg)
25. Name/Position Comments
Company/Division
Email/Telephone
Short term
(<1year)
Medium term
(<3years)
Long terms
( >3 years)
B. TIMELINE: I am interested in using the presented software tools:B
As ‘stand alone’ tool As ‘plug-in’ tool As ‘integrated’ tool
Feedback
Simulation Tools: (1) Variation Response method (VRM); & (2) Deep learning in Manufacturing (DL in Mfg)