| 1. |
- Castañé, G., et al.
(författare)
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The ASSISTANT project: AI for high level decisions in manufacturing
- 2023
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Ingår i: International Journal of Production Research. - : Taylor & Francis. - 0020-7543 .- 1366-588X. ; 61:7, s. 2288-2306
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Tidskriftsartikel (refereegranskat)abstract
- This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.
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| 2. |
- Vyhmeister, Eduardo, et al.
(författare)
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Lessons learn on responsible AI implementation : the ASSISTANT use case
- 2022
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Ingår i: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022. - : Elsevier. ; , s. 377-382
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Konferensbidrag (refereegranskat)abstract
- Currently, pioneer companies are working hard to construct applied ethical frameworks in different sectors for using AI components that generate trust in their clients and workforce. However, independent of these few companies, there is still a considerable gap between understanding the impact of using responsible AI components, the implications of the lack of use, and what is currently applied in the industrial sector. Given that industry has shown an increased commitment to incorporating AI components, works focus on broadening the understanding of manufacturing sector stakeholders of what approaches could be considered within AI life-cycle, reducing the gap between principles and actionable requirements, and defining fundamental considerations based on risk management for incorporating, and managing, AI-based on responsible AI are required. In this work, we present a summary of the most suitable approaches that can be used for implementation and the lessons learned from a European Funded project (ASSISTANT).
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| 3. |
- Östberg, Per-Olov, et al.
(författare)
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Domain Models and Data Modeling as Drivers for Data Management : The ASSISTANT Data Fabric Approach
- 2022
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Ingår i: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022. - : Elsevier. ; , s. 19-24
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Konferensbidrag (refereegranskat)abstract
- To develop AI-based models capable of governing or providing decision support to complex manufacturing environments, abstractions and mechanisms for unified management of data storage and processing capabilities are needed. Specifically, as such models tend to include and rely on detailed representations of systems, components, and tools with complex interactions, mechanisms for simplifying, integrating, and scaling management capabilities in the presence of complex data requirements (e.g., high volume, velocity, and diversity of data) are of particular interest. A data fabric is a system that provides a unified architecture for management and provisioning of data. In this work we present the background, design requirements, and high-level outline of the ASSISTANT data fabric - a flexible data management tool designed for use in adaptive manufacturing contexts. The paper outlines the implementation of the system with specific focus on the use of domain models and the data modeling approach used, as well as provides a generic use case structure reusable in many industrial contexts.
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