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| Organisation: | Laboratory for Manufacturing Systems & Automation (LMS) - University of Patras - PANEPISTIMIO PATRON |
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Operators and engineers are coming in contact with far more powerful but also more complex technologies both in terms of hardware and software. Natural interaction is a necessity in order to exploit the value added by such technologies and reduce the cognitive requirements on the human side. Seamless, AI enabled, human like interaction which is able to handle and reason over multiple data sources (from the production system, resource and process levels) will significantly boost productivity, efficienct and well being. Such solutions need to be supported by proper digital literary and upskilling using advanced methods allowing a hands on training (e.g. Teaching Factory) on the configuration and use of introduced AI and HMC technologies.
Cross sectoral technology diffusion is a major means of innovation promoting efficiency at reusing knowledge, resources and technological advancement. Lighthouse projects are instrumental in increasing visibility and promoting this transfer however they should provide structured means for different companies/organizations to be involved (e.g. open calls, matchmaking etc) and actively engage in this cross fertilization activity
Responsiveness depends on flexibility and reconfigurability but all links in the value chain must be reconfigurable in order to have a responsive system. Each node of the value network requires different approach/tools/technologies to operate in a reconfigurable way. Therefore enablers both digital (Digital Twins, AI decision making) and physical (robotics, human robot collaboration means, reconfigurable equipment) need to be developed to support the value chain.
Manufacturing equipment and automation is becoming AI enabled, meaning that it can generate and consume much larger amounts of data than before. This can be exploited to monitor and manage in real time the production and logistics processes by considering both local and global system status. AI decision making algorithms should be able to assess productivity and efficiency at both levels and automatically generate process plans which can be assigned and dispatched to autonomous resources for real time reconfiguration. Digital Twins should be used not only as a means of mirroring actual system status but also for enabling the simulation of what-if scenarios and the optimization of processes at multiple levels.
Manufacturing activities will always remain a cornerstone for EU prosperity and leaderhip must be retained or extended to all sectors. Innovative production systems with human centric characteristics allowing people to remain productive regardless of their age/gender/special condition will play an important role. Technologies to integrate human in a smart working environment where resources (e.g. robots) collaborate with each other and humans in a seamless way will have a great impact on the way Europe manufactures its goods.
Optimization needs to take place both horizontally (e,g, shopfloor control) and vertically (management of operations). Digital twins able to capture information from each level and process it for decisions making using AI are needed to simplify and make the orchestration more effective.
Operators and engineers are coming in contact with far more powerful but also more complex technologies both in terms of hardware and software. Natural interaction is a necessity in order to exploit the value added by such technologies and reduce the cognitive requirements on the human side. Seamless, AI enabled, human like interaction which is able to handle and reason over multiple data sources (from the production system, resource and process levels) will significantly boost productivity, efficienct and well being. Such solutions need to be supported by proper digital literary and upskilling using advanced methods allowing a hands on training (e.g. Teaching Factory) on the configuration and use of introduced AI and HMC technologies.
Already significant results have been achieved in the field through advanced robotics solutions (high payload collaborative robots, exoskeletons, mobile robots etc). The challenge is to bring them to the final stage so that they can seamlessly work with (and for) humans with minimal efforts (both cognitive and physical) required on the operator side. Maturation of the technologies need to be followed by convincing demonstration of the technologies both in industrial and open pilot environments to achieve wide acceptance at the different organizational levels.
In parallel with the digitally enabled upskilling, ways to better include and immerse humans in social manufacturing environments are needed. Cognitive augmentation in different forms can allow humans to do more with less requirements on their side. Technology working for the humans to allow them to achieve greater impact.
Cross sectoral technology diffusion is a major means of innovation promoting efficiency at reusing knowledge, resources and technological advancement. Lighthouse projects are instrumental in increasing visibility and promoting this transfer however they should provide structured means for different companies/organizations to be involved (e.g. open calls, matchmaking etc) and actively engage in this cross fertilization activity
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THOMAS - Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems
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SHERLOCK - Seamless and safe human - centred robotic applications for novel collaborative workplaces
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CONVERGING - Social industrial collaborative environments integrating AI, Big Data and Robotics for smart manufacturing
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MASTERLY - Nimble Artificial Intelligence driven robotic solutions for efficient and self-determined handling and assembly operations
Manufacturing equipment and automation is becoming AI enabled, meaning that it can generate and consume much larger amounts of data than before. This can be exploited to monitor and manage in real time the production and logistics processes by considering both local and global system status. AI decision making algorithms should be able to assess productivity and efficiency at both levels and automatically generate process plans which can be assigned and dispatched to autonomous resources for real time reconfiguration. Digital Twins should be used not only as a means of mirroring actual system status but also for enabling the simulation of what-if scenarios and the optimization of processes at multiple levels.