Periodic Reporting for period 1 - AIMS5.0 (Artificial Intelligence in Manufacturing leading to Sustainability and Industry5.0)
Reporting period: 2023-05-01 to 2024-04-30
AIMS5.0 a collaborative Innovation Action aims at strengthening European digital sovereignty in comprehensively sustainable production, by adopting, extending and implementing AI-enabled hardware and software components and systems across the whole industrial value chain to further increase the overall efficiency.
Vulnerability of existing supply chains in crisis shows the need for shorter supply chains and for keeping production in Europe. AI enabled fabs will be given more output and higher sustainability, which makes them more competitive on a global scale. New technologies from IoT and based on semantic web ontologies, ML and AI will help to enable the transformation from Industry4.0 to Industry5.0 to create human-centric workplace conditions and to enable the transformation of European industry to climate-friendly production. Above all, sustainability and resilience will be improved.
In essence, AIMS5.0 will deliver:
- AI-enabled electronic hardware components & systems for sustainable production
- AI tools, methods & algorithms for sustainable industrial processes
- SoS-based architectures & micro-services for AI-supported sustainable production
- Semantic modelling & data integration for an open access productive sustainability platform
- Acceptance, trust & ethics for explainable industrial AI leading to human-centered sustainable manufacturing
Vulnerability of existing supply chains in crisis shows the need for shorter supply chains and for keeping production in Europe. AI enabled fabs will be given more output and higher sustainability, which makes them more competitive on a global scale. New technologies from IoT and based on semantic web ontologies, ML and AI will help to enable the transformation from Industry4.0 to Industry5.0 to create human-centric workplace conditions and to enable the transformation of European industry to climate-friendly production. Above all, sustainability and resilience will be improved.
In essence, AIMS5.0 will deliver:
- AI-enabled electronic hardware components & systems for sustainable production
- AI tools, methods & algorithms for sustainable industrial processes
- SoS-based architectures & micro-services for AI-supported sustainable production
- Semantic modelling & data integration for an open access productive sustainability platform
- Acceptance, trust & ethics for explainable industrial AI leading to human-centered sustainable manufacturing
During the first reporting period significant progress toward the achievement of the objectives were made:
Objective 1 - AI-enabled electronic components and systems for sustainable production: Questionnaires were made to survey the hardware platforms employed by the partners. The result revealed that most hardware platforms adopted are Em-bedded devices with Edge CPU/GPUs, such as Raspberry Pi or Nvidia Jet-son Nano.
Various hardware platforms have been evaluated based on energy efficiency and latency. The outcome reveals that FPGAs can deliver similar latency results to GPUs with higher energy efficiency.
Objective 2 - AI tools, methods and algorithms for sustainable industrial processes: Several questionnaires have been created regarding achievement plans and activities. Their analysis revealed the way AI Toolbox is supposed to be built, and how the partners could achieve their AI / ML integration goals within the project. To support AI service design and implementation, the Toolbox now includes some well-defined functionality regarding design templates, examples, and reusable tools.
Objective 3 - SoS-based architectures and micro-services for AI-supported sustainable production: The progress in WP3 in the reporting period was good and an early evaluation and usage of the common Cyber architecture in support of several use cases is ongoing. The Arrowhead Roadmap (v5.0) is publicly available since Octo-ber 2023 at: https://github.com/eclipse-arrowhead/roadmap/tree/main/5.0%20Draft(opens in new window). As a part of the AI-gym solution a model-based AI Gym concept was proposed (see D3.1 for more details). The system is now accessible from out-side: https://prototaip.researchstudio.at/hub/login(opens in new window).
Objective 4 - Semantic modelling and data integration for an open access productive sustainability platform: The initial step involved the in-depth analysis and evaluation of the Digital Reference (DR), the foundational element of the Open Access platform (OAP), for potential expansions. Subsequently, three OAP focus groups were established in partnership with FUH, NXP, and DAC, each focusing on the domains of Planning, Sustainability, and Quality. Within each group, specific use cases were diligently developed, and an ontology was constructed to support their respective areas of focus.
Objective 5 - Application and validation of innovative AI solutions for digitalized, sustainable, and resilient manufacturing in different industrial domains: During the reporting period, we have successfully integrated a mobile platform-based scan robot into the productive environments of our factories. This integration has allowed us to analyse manual scan processes and optimize the utilization of autonomous scanning processes in various technology areas. The work in the first year started on all activities within the different tasks and use cases according to the planned timeline. The results were documented within the first deliverables for specification and requirements, and give a comprehensive overview about the individual status of the use cases.
Objective 6 - Acceptance, trust and ethics for industrial AI leading to human centered sustainable manufacturing: Interviews were conducted and analysed. The results have been reported in a conference paper for IEEE/IFIP MFI5.0 Workshop at the NOMS (held on May 6 – 10, 2024, Seoul, South Korea). Assessment of the standardization landscape and the recommendations and guidelines with respect to WP 7 goals (AI, ethics, public stakeholders’ interests) was done and documented.
Objective 1 - AI-enabled electronic components and systems for sustainable production: Questionnaires were made to survey the hardware platforms employed by the partners. The result revealed that most hardware platforms adopted are Em-bedded devices with Edge CPU/GPUs, such as Raspberry Pi or Nvidia Jet-son Nano.
Various hardware platforms have been evaluated based on energy efficiency and latency. The outcome reveals that FPGAs can deliver similar latency results to GPUs with higher energy efficiency.
Objective 2 - AI tools, methods and algorithms for sustainable industrial processes: Several questionnaires have been created regarding achievement plans and activities. Their analysis revealed the way AI Toolbox is supposed to be built, and how the partners could achieve their AI / ML integration goals within the project. To support AI service design and implementation, the Toolbox now includes some well-defined functionality regarding design templates, examples, and reusable tools.
Objective 3 - SoS-based architectures and micro-services for AI-supported sustainable production: The progress in WP3 in the reporting period was good and an early evaluation and usage of the common Cyber architecture in support of several use cases is ongoing. The Arrowhead Roadmap (v5.0) is publicly available since Octo-ber 2023 at: https://github.com/eclipse-arrowhead/roadmap/tree/main/5.0%20Draft(opens in new window). As a part of the AI-gym solution a model-based AI Gym concept was proposed (see D3.1 for more details). The system is now accessible from out-side: https://prototaip.researchstudio.at/hub/login(opens in new window).
Objective 4 - Semantic modelling and data integration for an open access productive sustainability platform: The initial step involved the in-depth analysis and evaluation of the Digital Reference (DR), the foundational element of the Open Access platform (OAP), for potential expansions. Subsequently, three OAP focus groups were established in partnership with FUH, NXP, and DAC, each focusing on the domains of Planning, Sustainability, and Quality. Within each group, specific use cases were diligently developed, and an ontology was constructed to support their respective areas of focus.
Objective 5 - Application and validation of innovative AI solutions for digitalized, sustainable, and resilient manufacturing in different industrial domains: During the reporting period, we have successfully integrated a mobile platform-based scan robot into the productive environments of our factories. This integration has allowed us to analyse manual scan processes and optimize the utilization of autonomous scanning processes in various technology areas. The work in the first year started on all activities within the different tasks and use cases according to the planned timeline. The results were documented within the first deliverables for specification and requirements, and give a comprehensive overview about the individual status of the use cases.
Objective 6 - Acceptance, trust and ethics for industrial AI leading to human centered sustainable manufacturing: Interviews were conducted and analysed. The results have been reported in a conference paper for IEEE/IFIP MFI5.0 Workshop at the NOMS (held on May 6 – 10, 2024, Seoul, South Korea). Assessment of the standardization landscape and the recommendations and guidelines with respect to WP 7 goals (AI, ethics, public stakeholders’ interests) was done and documented.
AIMS5.0 will result in lower manufacturing costs, increased product quality through AI-enabled innovation, decreased time-to-market and increased user acceptance of versatile technology offerings. They will foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal and push the industry towards Industry5.0.
The innovations will leverage the experience of the 53 partners, such as renowned OEMs, Tier-1 and Tier-2 suppliers, technology and application large enterprises and SMEs, supported by academic research specialists in fields like AI, industrial hardware and software, decision making and management algorithms.
Specific outcomes of the project are
- 20% faster time to market,
- Participation of disabled people in the factory environment > 5% (in relation to the total number of employees employed in production),
- AI based MES capability > 10 %,
- Increased user awareness and trust by 10%,
- Subsequent reduction of environmental footprint for wafer transport, handling and storage > 20 %,
- 50% reduction of time for monitoring industrial equipment.
The innovations will leverage the experience of the 53 partners, such as renowned OEMs, Tier-1 and Tier-2 suppliers, technology and application large enterprises and SMEs, supported by academic research specialists in fields like AI, industrial hardware and software, decision making and management algorithms.
Specific outcomes of the project are
- 20% faster time to market,
- Participation of disabled people in the factory environment > 5% (in relation to the total number of employees employed in production),
- AI based MES capability > 10 %,
- Increased user awareness and trust by 10%,
- Subsequent reduction of environmental footprint for wafer transport, handling and storage > 20 %,
- 50% reduction of time for monitoring industrial equipment.