Using Reinforcement Learning for Optimization of a Workpiece Clamping Position in a Machine Tool

Samsonov, V.; Enslin, Chrismarie; Köpken, Hans-Georg; Baer, Schirin; Lütticke, Daniel

doi:10.5220/0009354105060514

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…The aim was to limit the resulting force in press-and-release systems, obtaining a resulting final roughness reduction of 30.68%. A further example of RL application was described in Reference [86] to predict the optimal clamping position of the workpiece.…”

Section: Modelingmentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

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This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

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Section: Modelingmentioning

confidence: 99%