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April 10, 2020 | To combine agility and competitivity in manufacturing industry? The EU DIMOFAC project takes up the challenge!

dimofac illustrationMass customization in a context of mass individualization or reorienting its production capacities to respond to a shortage in times of crisis are real challenges! How to reconfigure production tools while remaining competitive?

The solution is to combine a more agile organization and modular production lines with digital simulation to optimize the industrial manufacturing processes. The EU DIMOFAC project, which was officially launched in November 2019 at the CEA Grenoble premises (France), will develop enabling technologies.

The project aims at providing manufacturers with digital solutions for modeling and reconfiguring the production tool through the digital twin. This tool dynamically reproduces the production line at all points, making it possible to monitor operations, test different scenarios, and even control and reconfigure the physical line.

The ambition of the CEA List and Liten Carnot Institutes teams is to lay the foundations for a standardization of industrial architectures. "The idea is to make this digital twin and production systems modularity replicable, based on the Model Based System Engineering (MBSE) methodology" explains CEA List’s researcher Arnaud Cuccuru.

Demonstrators will be implemented on project partners' production lines, thanks to the tools developed by the CEA, such as CIVA for automated on-line non-destructive testing (NDT) and mostly, Papyrus for the functional digital twin of manufacturing production lines.

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April 9, 2020 | Bringing order to industrial tasks

Factory Lab GECO List 250A software suite to automate the optimization and validation of industrial process task scheduling was developed at FactoryLab.

The automotive and aeronautics industries have particular throughput requirements. To meet these requirements, process tasks must be scheduled in a specific way. Tasks are scheduled to ensure that production is as efficient as possible. And scheduling factors in a multitude of parameters, from the human and material resources available to safety restrictions that would prevent two tasks from being completed simultaneously, for example. Finally, task scheduling, often based on experience, is very complicated to do manually. CEA List, a CEA Tech institute, recently completed an R&D project at FactoryLab with the goal of automating task scheduling.

FactoryLab community members PSA and Safran partnered with List on a joint project called GECO. The project's objectives were to reduce the amount of time it takes to optimize and validate task scheduling on PSA's automotive assembly lines and on Safran's motor maintenance lines. The researchers tailored their Papyrus-based software suite, which creates a digital twin of a production line, to the specific needs of the two cases addressed here.

This digital twin uses models and simulations to generate variants of all parameters and constraints, accurately analyze a complex situation, and predict and resolve any conflicts between tasks. This smart, automated system allocates resources and plans tasks, using heuristics developed with artificial intelligence. Unforeseen events (orders requiring fast turnarounds, a change in human or material resources) can be factored in to rapidly update the task schedule.

About FactoryLab:

FactoryLab, a consortium whose members represent industrial companies and academic research institutions, has the capacity to very rapidly integrate technological solutions and to build prototypes and demonstrator systems to support its members' transformation strategies. The consortium facilitates the adoption of these new solutions and helps shorten time to market.

FactoryLab spans diverse industries and markets, making it a novel space where ideas cross traditional barriers and where members can access shared resources. Whether they are technology providers, integrators, or industrial end-users, FactoryLab positions all of its users to create value.


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April 2, 2020 | Leaf: Verification of mixed-criticality embedded systems

Leaf cea list 250A tool for the analysis and formal verification of the time properties of embedded computers is currently being developed at CEA List. The goal is to control cohabitation and interactions by addressing the hierarchy between functions with different criticality levels.

Increasingly complex hardware architectures and command-control algorithms are creating new challenges for the aeronautics, automotive, and other industries. Not least of which is the widening gap between average performance and worst-case scenarios. This is particularly problematic for embedded systems, where functions with different criticality levels and a variety of performance requirements must live on the same computer. The response times for embedded systems must be well-defined and controlled. However, the computing resources are dimensioned according to the worst-case response times, which are significantly overestimated to provide a safety buffer.

A more detailed assessment of the computers' time properties (anomalies, memory contention, etc.) would enable more efficient use of the available computing resources and, in the process, make it possible to reduce the safety buffer. List turned to the University of California, Berkeley for its world-leading scientific research and targeted expertise in predictable architectures. Researchers at List subsequently started development work on Leaf, a tool that makes clever use of formal methods to very accurately reproduce, analyze, and verify program behaviors on a given computer.

Ultimately, Leaf should be able to provide information that will help manufacturers design and dimension their systems and, therefore, increase trust in system behavior.

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March 23, 2020 | Better supervision of medium-voltage electrical distribution networks

reseaux moyenne tension 250 cea listA system for detecting transitory faults on medium-voltage electrical distribution networks was developed to prevent power outages before they occur. It can detect and locate very early indicators of wear or damage to cables.

An ounce of prevention is worth a pound of cure! Cable manufacturer Nexans turned to CEA List to develop an innovative monitoring system for medium-voltage electrical distribution networks. The system detects extremely brief transitory faults (from tens of nanoseconds to a few microseconds in duration) that conventional monitoring systems cannot pick up.

Even very minor, extremely brief current and voltage fluctuations are detected by sensors installed at different points on the network. What makes the method developed by List so original is that an external GPS clock is used to regulate the timing of the autonomous sensors. Triangulation based on the mathematical principle of time reversal effectively leverages the time differences between when a signal is received by different sensors to determine the location of a fault to within several centimeters for networks up to 10 km long.

The information is automatically transmitted and saved to a database managed by Nexans, which operates the data processing systems created by the CEA. The system is currently able to provide information on the location, intensity, and frequency of faults. The researchers will soon be able to determine the type of fault detected, as well. Nexans is now exploring how to best make use of this new tool. One idea is to develop a mobile maintenance service that would be made available to Nexans customers a few weeks at a time.

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March 20, 2020 | Phoebe: a complete dose-simulation code

phoebe cea list 250A powerful and modular new computing code was developed to simulate the dose of radiation received by patients during radiation therapy and medical imaging procedures.

Researchers at List, a CEA Tech institute, developed a particle transport code that uses the Monte Carlo method. The goal is to simulate the dose of radiation received by patients during radiation therapy and medical imaging. To ensure optimal control of the simulation code in both technical terms (physical models and geometries) and in legal terms (licensing), the researchers developed a new code called Phoebe (PHOton and Electron Beams).

They used the validated physical models from Penelope, the Monte Carlo code most often used in radiation therapy, as the starting point for the new code, which was written in a programming language that is more portable and modular. For instance, because of the programming language chosen, Phoebe can be used on all operating systems, from PCs to smartphones. And, because Phoebe is modular, new features can be integrated later on. In fact, Phoebe was recently given a model to simulate physical phenomena at a cellular scale used to determine how a patient will respond to radiation-therapy-enhancing nanoparticle injections. List engineers are currently developing new models to factor in the entire dose received by the patient during a radiation treatment, including in areas far away from the tumor.

Phoebe came through laboratory validation testing with flying colors and the technology is mature enough at this stage for the models to be made available to a broad community of users via an open source platform.

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