The project ArchiTOOL aims at inventing, prototyping, and evaluating an immersive and intelligent virtual environment for architecting complex technological systems. Instead of using domain-specific engineering software, the immersive and interactive environment will provide the architect with the modelling capabilities required to define the various views (operational, specification, functional, behavioural, structural, logic, safety, etc.) of a system architecture in a single virtual space before exporting each viewpoint in a standardised format that will enable domain-experts to continue with a detailed design. Moreover, the immersive environment will include a cognitive agent to support the system architect with intelligent capabilities: models verification, context-aware recommendation of rules, identification and automation of modelling routines...

This project is proposed in the context of the Industry 4.0. It more directly concerns the introduction of new technologies from robotics and information & communication science in the workshops for internal logistics operations. For next generation production systems, mastering the internal logistics is still a crucial issue. Its related costs are far from being negligible regarding the operating costs in classic industries. And its influence on workshops performance through the mastering of physical flow is critical. The transportation activities are also known to be painful for humans and sometimes hard to automate. Although automatic transfer system exists, for example in microelectronics plants to convey wafers, it is possible to assume that nowadays a huge part of the transfer operations are poorly automated. One interesting question is to fully understand how the decision to automate or not the internal logistics operations is made. Additionally, with the new possibilities brought by industry 4.0 and namely by full connectivity of production actors, it is important to renew the question of automating some of the transportation operations, with production context & state aware systems. The arrival of industry 4.0 concepts changes the historical considerations around LoA (Level of Automation). New technologies are offered to automatically perform some physical actions, with better set up time (“plug and produce” approach) and with automated decision of action thanks to a broader knowledge of the productive environment (“Internet of things” (IoT) applied in production system). Industrial resources such as machines and transportation systems are being used more intelligently taking advantage of the shop floor connectivity. Associated to this technical point of view, the collaboration between operators and robots must also be rethink since they are supposed to have deeper interactions in their activities. These new aspects of the industry of the future have to be integrated in a methodology to design and implement suitable and relevant internal logistics systems. It is ambitioned in the project to analyze the added value of communication strategies between the transportation system and the production system and the cooperation to be defined with operators. The flexibility, efficiency, and robustness of these systems shall be analyzed. Namely, the performance of a production system equipped with a mixed transfer system combining operators and connected automated transporters, in presence of unexpected events will be addressed. In this project, it is proposed to deeply analyze the design of internal transportation systems for production sites, through the prism of LoA decision. The ambition is to deliver a design and decision process to make sound automation decision for transportation activities for various industrial contexts. Alongside, to a detailed design approach, it appears very relevant to also propose best practices. For example, it seems valuable to describe generic scenarios (relevant representative industrial case) with their associated recommended practice in term of design. This project will use 2 types of validation and experimentation field. The first will be the use of an operations management emulation platform currently developed in our lab. The second will be experiments at industrial partners’ plants. The global objective of the project can be summed up as developing a methodology to implement internal logistics system for manufacturing plants in an industry 4.0 context, based on an analysis of the relevant Level of Automation.

Current challenges in metal additive manufacturing processes can be mainly traced back to complex multi-physics coupling induced by the thermal loading history of the produced part. Caused defects, based on these phenomena, are mostly insufficient geometrical accuracy and low or unpredictable strength properties, which prevent the broad use of the technology in an industrial scale for manufacturing end products. Due to fluctuating material properties and geometry-dependent process parameters, the need for online closed-loop process control and enhanced process strategies is indispensable to achieve high part qualities. The aim of this project is to setup this real-time closed-loop control for freeform metal additive manufacturing processes. This approach will be achieved by the combination of ultra-fast thermal process modeling and planning, accurate in-situ measurements as feedback signal, and by online real-time optimization of selected process parameters and path strategies.

Collaboration 4.0 project is a contribution to the main industry of the future challenge of the efficient place of humans in the factory of the future. Industry of the future means the high-tech digitalization of production systems to get more flexibility of the whole value chain to achieve personalized products and sustainability. Enabling technologies like Internet Of Things, wearables, robotics, Artificial Intelligence and 3D Printings are the key drivers of this industrial transformation. The main challenge is to keep the economic value of mass production (3.0) when competitive lot-size 1 personalized production (4.0). The Collaboration 4.0 project aims at studying working situations enabled by the new digital technologies in 4.0 industrial environment for their productivity and attractive features. The project addresses the Nb 3 ANR research axis “Fostering industrial renewal” and especially the Nb 1sub-axis “Factory of the future: Human, organization and technologies”. Its overarching objective is to design collaborative workplaces of the future in which workers and machines are closely combined to reach new sustainable performance in 4.0 industrial environment. The project is featured from three fundamental research hypotheses: 1) The Human-Machine collaborative activity of the future will be carried out in a new enabling competence-based industrial environment, 2) Digital technologies are flexible and frequently evolve, 3) Work and industrial organization highly influences the well performing Human-Machine collaborative activity. The project aims at designing new workplaces in which workers and machines share the same space to complete shared tasks by using work-enabling digital technologies. The worker will manage work activities controlling the machine tasks and instructing it. The machine is designed to meet the worker needs. It could provide worker with new ways of working. We want to define and characterize the new types of 4.0 collaborative workplaces useful and well performing in a specific industrial situation. The core issues are the efficient technology uses while producing and the industrial organization to be set up. Concretely, the project will study two different work situations from two case studies: a collaborative activity between a robot and a human on one side and between an augmented reality wearable and a human on the other side. Delivered results will be a classification of human-Machine collaborative work situations in an enabling industrial environment, a framework for analyzing an enabling collaborative industrial activity and recommendations for designing enabling industrial workplaces. The project is a multidisciplinary project combining industrial engineering, ergonomics and digital technologies. It is featured in five scientific tasks and one management task. A workplace-of-the-future demonstrator will be developed at the Grenoble INP S.MART technological platform from existing facilities. An industrial advisory board accompany the research partners to operationalize the theoretical propositions. It is a 48-month project and relies on two PhD thesis and an engineer position. The project will be managed by G-SCOP laboratory (industrial engineering, augmented reality) alongside with LIG (robotics and HMI) and ACTé (ergonomics). Each laboratory will bring to the project their human resources and equipment as necessary. Project results will be spread through scientific publications, guidelines for industrial companies and communication activities.

In Additive Manufacturing, Directed Energy Deposition (DED) is a promising technology that gains a growing interest in industry. An essential feature of this process its rapid fabrication capability, even for large-size parts. However, generating good material deposition trajectories remain a huge challenge that CAM software often fail to correctly deal with. The KAM4AM project aims at developing a software for DED manufacturing, based on the proven Artificial Intelligence technology of Reinforced Learning, to get a learning and adaptive CAM solution. A list of study cases from industry will help to collect the typologies of parts as well as technical and scientific issues related to DED technology. This data, combined with research cases, will enable to define the objectives and the functions of the learning environment that needs to be created. The main research challenges are (1) to design a problem-independent reward system, based on expert rules of the DED domain, (2) to develop a phenomenological model of the DED process, fast enough for allowing the numerous iterations required for the learning process. A last step consists in a thorough test of the generated trajectories, followed by the integration of these trajectories into Esprit Additive software.