Collaboration with intelligent and autonomous systems: Responsible human-technology relationships in automated industrial contexts including experience measures and impact assessment methods.

Requirements within topics on the project level:

Emphasis on learning organizations and skilled humans (incl. managers and operators): providing a framework for meaningful integration of emerging technologies into work processes of organizations with the focus on improving the needed skills, work situations and efficiency of the solutions.

Minimize waste (zero-defect) at speed

Speeding while innovating • Robust and Adaptive learning and control

Interrelated challenge: design & materials & manufacturing – no time to learn, design for productivity

From product to process data inspection, modelling, monitoring and control

Biocompatible/ recycled materials /biomaterials

From ex-situ to in-situ data mining (sensing/edge)

From data to information to knowledge

Knowledge transfer and knowledge fusion

Product & process digital twin

Smart data-driven intralogistics, shift from manufacturing to intralogistics to guarantee high flexibility and timeliness.

Advanced innovative planning methods for shared resources.

Holistic, integrated view on product-process-production (3Ps) 

There are different initiatives and standards to support efficient manufacturing. However, exchange of information is still slow.

Integration of information flows throughout the product lifecycle can help 

• to improve productivity and allow efficient development of new products.
• to address bigger challenges such as circularity, including re-manufacturing, re-design and re-use.

Improvements for any of those „3Ps“ (product-process-production) requires to establish and analyze relations to other two 'P's'

• Utilize AI and dataspaces for transparent and resilient supply chains.
• Metrics of Resilience 

• Efficient management of flexibility in manufacturing systems 
• Controlling complexity in cognitive cooperating machines and robots 
• Robotics and Automation: Dynamic flexible shop floor
• Advanced digital models and tools – model complex/deformable materials
• Full integration of cyber-physical systems, digital twins, and AI models.
• Develop explainable AI-driven tools (e.g. Generative AI) for process optimization, quality control, and supply chain management.
• Metrics of productivity 

Example: A single mechanically or/and electrically defective joint can compromise the performance of an entire battery pack which includes hundreds of them. Inspecting each one individually is not efficient neither possible.

Robotics and Automation: Consider non-rigid materials.

• Manufacturing processes optimization.   Sensing, Feature engineering & model training, Classify electrical/mechanical quality.
• Manufacturing Systems: Productivity improvements through OEE.  A Deep Learning Model for Predictive Maintenance
• Develop explainable AI-driven tools (e.g. Generative AI) for process optimization, quality control, and supply chain management.

• Manufacturing Systems: Productivity improvements through advanced production scheduling
• Utilize AI and dataspaces for transparent and resilient supply chains.

Full integration of cyber-physical systems, digital twins, and AI models.

Manufacturing as a Service (MaaS)

Zero Defect Manufacturing. Manufacturing process monitoring for ZDM.

Digital updating of manufacturing assets (increase productivity with less CAPEX, prolongation of machinery production) instrumentation, data analysis , digital twin.

Structural weight reduction of transports manufacturing of light composite parts, upscaling , part functionalisation to reduce assembly and create smart parts for SHM (Structural Health Monitoring)

High variability and dexterity in footwear require advanced automation solutions

Advanced automation solutions should increase attractiveness of jobs in footwear industrie, reducing repetitive task and high specialisation requirements.

• Advanced and smart material processing technologies and process chains 
• Smart mechatronic systems, devices, and components 
• Data analytics, including artificial intelligence and deployment of digital platforms for data management and sharing (data integration) 
• Simulation and modelling (digital twins) covering the material processing level up to manufacturing system and, factory level 
• Innovative sensors, sensor materials and innovative inception methods (machine vision in combination with AI) 
• Intelligent and autonomous handling and robotics, assembly and logistic technologies

Support the development of equipment (TRL3-TRL5)

• Components and subsystems (for Hydrogen, electric vehicles, hybrid grids)
• Materials: sustainability / recyclability /multifunctional
• Process–digitalisation/automation (competitiveness)

Pave the way to industrialization (TRL6-TRL8) –PRIVATE-PUBLIC partnerships

Manufacturing for green products and technologies:

• Up to four-fifths of a product’s lifetime emissions are determined by decisions made at the design stage
• Most of the green choices on product materials, design and performance should be abandoned or need an impressive amount of R&D in manufacturing

New manufacturing solutions for a new generation of complex products for:

• Lightweight
• Thermo-mech
• metamaterial
• multimaterial
• First-time-right

Energy sector:

• Additive Manufacturing
• Powder Processing
• Sensors

(For recycling and lean processes)

Automotove, Aerospace, Railway: Potential topics (about polymer composites manufacturing)

• Development of in-situ monitoring (i.e. process-induced defects detection) technology for high production rate or high performance product manufacturing processes (e.g. filament winding, compression RTM, Carbon/PAEK AFP)
• Development of numerical models to predict the generation of defects during the manufacturing process and assess their influence on the quality or residual lifetime of a final product, (and eventually determine the tolerance of the defects), in order to reach “zero-waste” manufacturing
• Development of digital twin to establish white box or gray box models via data science, in order to reduce the computational time for numerical simulation and real-time optimization of manufacturing processes

• reshoring Europe’s competitive manufacturing: exploiting digitalization in full sustainable product development taking product design from master-slave to peer-to-peer requires novel methodologies and models
• how to support European industry in the transition from the old to the new (infra, business, people)

Aircraft developments: 

• Advanced air mobility
• Electric propulsion
• Hydrogen-powered propulsion
• Application of novel materials and manufacturing concepts

Sustainability objectives less dominated by operation

Challenge for MiE: innovative Transport needs integrated balance between

• Sustainable manufacturing objectives
• Sustainable operation objectives
• Other specific requirements (e.g, for safety)

• Rapid reconfiguration and deployment of flexible production systems, assuring quality and robust performance
• Data spaces and cloud/edge solutions for responsive and robust manufacturing

Recovering and preserving the European leadership in strategic and high value added products -
Renewable energy generation and storage and critical machinery components

• Development and deployment of innovative and automated de- and re-manufacturing operations, including the physical collection of products, disassembly, separation and sorting
• Digital passport to assure high quality, traceability and compliance with quality standards

• Energy transition will create many new areas of application for batteries.
• Step from cutting edge research to industrial implementation necessary for a competitive battery ecosystem.
• More European players producing cells of sufficient quality with low costs in large scale are needed.
• Without natural local competitive advantages (low electricity costs), a strong political will is needed to make battery production competitive

Decarbonization:

• Circularity applied to the Batteries domain (second life / recycling)
  • Circularity by design
  • Automation (in production, considering Circularity as part of manufacturing)
  • Automation (in the reusing and dismantling phase)
  • Management of CRM
• On-purpose vehicles
  • Small volume automation
  • Customization
• Components
• H2 (Fuel Cell and direct use)
  • Scaling-up production (automation)

Autonomous Driving / ADAS:

• Sensors
  • electronics manufacturing (big numbers, big automation)

• How to support the organization in its human centric strategy of transition to new technologies?
• How to reorganize tasks between humans and new technologies to get the best performance from the hybrid manufacturing system?

Remote operation (hybrid workplaces)

• How to work and interact in the merge of digital and physical worlds?

Need:

• Understand and support social and technological changes: new forms of social organisations; new business models (circular economy, functional economy, cooperative economy, etc.); digital responsibility of companies; digital transformation impact.
• Organisational change: Appropriation and daily use of innovation, performance as a whole (economy, social, environment).

Research focus:

• Digital transformations and organisations’borders: new management models; impact of AI and digital tools
• Innovation, Digital and Social Economy: digital transformation as a leveraging tool for social economy.
• Digital metamorphosis and ethical implication
• Corporate social responsibility

It would be interesting to give more importance to the point related to human-related barriers for the uptake of augmentation technologies in industrial environments such as ergonomics, user experience, comfort, trust, feeling of safety and liability in modern production facilities, taking into account age and gender aspects.

Challenge: Increasing complexity in human-cyber-technology systems and in the entire value chain. Lack of skilled professionals/ loss of expert knowledge due to demographic evolution 

Demonstration of integrated effects of human-centred solutions (e.g., health and well-being of workers, effectiveness, quality,…) 

How to implement human-centric?  IISE (Institute of Industrial and Systems Engineers (IISE) & IEA (International Ergonomics Association) have guidelines but a practical methodology is needed.     What are the effects on operators? How to support and not overload?

• Practical and efficient methodologies to facilitate compliance with the AI Act, Data Act, and relevant regulatory frameworks and Ethical principles.
• Ethical AI in the manufacturing workplace
• Common/open integration architectures --> reduce complexity of manufacturing system that employ latest technology --> allow humans to be easily engaged
• Contribution to safety standards towards truly co-existence within hybrid workspaces

Upskilling / reskilling / training tools to allow humans to catch up with technological evolution