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Self-learning Production Control Using Algorithms of Artificial Intelligence

Abstract : Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.
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Ben Luetkehoff, Matthias Blum, Moritz Schroeter. Self-learning Production Control Using Algorithms of Artificial Intelligence. 18th Working Conference on Virtual Enterprises (PROVE), Sep 2017, Vicenza, Italy. pp.299-306, ⟨10.1007/978-3-319-65151-4_28⟩. ⟨hal-01674863⟩

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