Smart Manufacturing is believed to play a critical role in maintaining the competitiveness of organisations, by supporting them at different levels such as process optimisation, resource efficiency, predictive maintenance and quality control. Nevertheless, AI technologies which are currently and rapidly penetrating industrial sectors at those levels remain essentially narrow AI systems. This is due to the lack of self-adaptiveness in the AIs capability to assimilate and interpret new information outside of its predefined programmed parameters. This mean that AI systems are tailored for solving specific tasks on a specific predefined setting and changes in the underlying setting usually requires system adaption ranging from fine-grained parameter adaptations to fully-fledged re-design and re-development of AI systems.
TEAMING_AI project aims at a human AI teaming framework that integrates the strengths of both, the flexibility of human intelligence and scale-up capability of machine intelligence. Human AI teaming is equally motivated to meet the increased need for flexibility in the maintenance and further evolution of AI systems, driven by the increasing personalization of products and service, as well as tackling the barriers of user acceptance and ethical challenges involved in the collaborative environments where artificial intelligence will be used, in order AI can be considered as 'teammate' rather than as a threat.
The TEAMING.AI project will be run over 36 months with a work plan divided into 9 Work Packages. Work Packages from 1 to 5 are devoted to the development of new technology to enhance the interaction between human and machine. Furthermore, Work Packages 6 and 7 wrap the development of 3 use case scenarios. Finally, two final Work Packages (8 and 9) will work respectively on the dissemination, exploitation of results and coordination of the project in a transversally way to the above mentioned WPs.
| Web resources: |
https://cordis.europa.eu/project/id/957402
https://www.teamingai-project.eu/ |
| Start date: | 01-01-2021 |
| End date: | 30-06-2024 |
| Total budget - Public funding: | 5 721 853,00 Euro - 5 721 853,00 Euro |
| Twitter: | @AiTeaming |
Original description
Smart Manufacturing is believed to play a critical role in maintaining the competitiveness of organisations, by supporting them at different levels such as process optimisation, resource efficiency, predictive maintenance and quality control. Nevertheless, AI technologies which are currently and rapidly penetrating industrial sectors at those levels remain essentially narrow AI systems. This is due to the lack of self-adaptiveness in the AIs capability to assimilate and interpret new information outside of its predefined programmed parameters. This mean that AI systems are tailored for solving specific tasks on a specific predefined setting and changes in the underlying setting usually requires system adaption ranging from fine-grained parameter adaptations to fully-fledged re-design and re-development of AI systems.TEAMING_AI project aims at a human AI teaming framework that integrates the strengths of both, the flexibility of human intelligence and scale-up capability of machine intelligence. Human AI teaming is equally motivated to meet the increased need for flexibility in the maintenance and further evolution of AI systems, driven by the increasing personalization of products and service, as well as tackling the barriers of user acceptance and ethical challenges involved in the collaborative environments where artificial intelligence will be used, in order AI can be considered as โteammateโ rather than as a threat.
The TEAMING.AI project will be run over 36 months with a work plan divided into 9 Work Packages. Work Packages from 1 to 5 are devoted to the development of new technology to enhance the interaction between human and machine. Furthermore, Work Packages 6 and 7 wrap the development of 3 use case scenarios. Finally, two final Work Packages (8 and 9) will work respectively on the dissemination, exploitation of results and coordination of the project in a transversally way to the above mentioned WPs.
Status
SIGNEDCall topic
ICT-38-2020Update Date
27-10-2022The TEAMING.AI framework was tested in three industrial use case scenarios selected to represent different levels and aspects of human involvement: quality control (Use Case 1) and machine and process diagnostics (Use Case 2). The third one (Use Case 3) focusses on harm prevention in dynamic production environments.
The method provides a modular approach for knowledge graph population and curation from complex and heterogeneous industrial/manufacturing domains. It helps different stakeholders to represent and share their mental models in a uniform standard representation that can be interpreted by both humans and AI agents.
The main contribution is a novel machine learning architecture that can process both vision data and sensor time-series data to concurrently to obtain greater defect detection accuracy.
High accuracy defect detection, which leads to reduced scrap rates and cost savings
Besides addressing the current lack of dynamic knowledge graph embedding methods, the partnerโs work also includes a lean updating approach to efficiently recognize knowledge graph modifications.
Knowledge graph embeddings can be computed on the fly to use them in downstream applications within dynamic domains such as manufacturing.
Even though state-of-the-art process orchestration tools are equipped with logging mechanisms, they do not come with semantically enhanced digital shadows of process knowledge. The aim is to close this gap. Process engineers do not need to become knowledge graph experts. They can just plug in our extension to automatically retrieve knowledge graph representations of their processes.
The current state of the art is limited in terms of team modelling and dynamic adaptability. The projectโ approach addresses both by integrating methods for process modelling and knowledge-update mechanisms to come up with an enriched digital shadow that goes beyond static models.
This software tool is designed for real-time process management and ensures the effective execution of these teaming processes. It performs continuous observation and analysis, allowing for the immediate detection of any deviations or disruptions in the team workflows. This recognition supports a prompt response, facilitating adjustments to maintain the operation of the platform.
Even though state-of-the-art process orchestration tools are equipped with logging mechanisms, they do not come with semantically enhanced digital shadows of process knowledge. The aim to close this gap. The knowledge graphs representation not only provides semantic linking/integration of heterogeneous manufacturing knowledge (process, product, resources) but also supports reusability, extensibility, and interoperability.
The development of advanced machine diagnostics software based on machine learning is intricately linked with hardware development. Accurate information obtained from machines and connected devices is crucial to the correct functioning of the software. Software sockets have been developed to link incoming data from injection machines with a data language that the system can understand.
The developed software sockets allow a seamless data acquisition from in-plant machines so data can be immediately consumed by the system. The data ingestion allows the AI models to be trained and used in a more simplified manner.
Usage of wide-angle cameras to monitor a very large work place and analyse multiple workers simultaneously, creating a digital shadow of a human-centred work process.
Rapid on-the-fly analysis of multi-person scenes on a large scale reduces occupational health officers' analysis time and allows for accurate work situation descriptions, as well as simplified follow-up improvement comparisons.
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| SOFTWARE COMPETENCE CENTER HAGENBERG GMBH (SCCH) (Coรถrdinator)
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| CORE INNOVATION
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| FARPLAS OTOMOTIV ANONIM SIRKETI (FARPLAS)
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| GLOBAL EQUITY & CORPORATE CONSULTING SL (SD)
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| GOIMEK S COOP
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| IDEA-INFORMATICS, DOMOTICS, ENVIRONMENT, AUTOMATION - SOCIETA COOPERATIVA
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| IDEKO
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| INDUSTRIAS ALEGRE SA
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| ITUNOVA TEKNOLOJI ANONIM SIRKETI (ITUNOVA)
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| PROFACTOR GMBH
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| TECHNOLOGICAL UNIVERSITY DUBLIN (TU Dublin)
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| TIME.LEX (time.lex)
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| TYRIS AI SL
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| TYRIS SOFTWARE SL (TYRIS)
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| UNIVERSITAET MANNHEIM
The TEAMING.AI framework was tested in three industrial use case scenarios selected to represent different levels and aspects of human involvement: quality control (Use Case 1) and machine and process diagnostics (Use Case 2). The third one (Use Case 3) focusses on harm prevention in dynamic production environments.