Autonomous optimization of cutting conditions in end milling operation based on deep reinforcement learning (Offline training in simulation environment for feed rate optimization)

Autonomous optimization of cutting conditions in end milling operation based on deep reinforcement learning (Offline training in simulation environment for feed rate optimization)

Full automation of manufacturing is strongly desired to improve the productivity. Autonomous optimization of the cutting conditions in the end milling operation is one of the challenges in achieving this goal. This paper proposes a system for optimization of the cutting conditions based on Deep Q-Ne...

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Bibliographic Details
Main Authors: Kazuki KANEKO, Toshihiro KOMATSU, Libo ZHOU, Teppei ONUKI, Hirotaka OJIMA, Jun SHIMIZU
Format: Article
Language:English
Published: The Japan Society of Mechanical Engineers 2023-09-01
Series:Journal of Advanced Mechanical Design, Systems, and Manufacturing
Subjects:
end milling
feed rate
optimization
deep q-network
simulation
Online Access:https://www.jstage.jst.go.jp/article/jamdsm/17/5/17_2023jamdsm0064/_pdf/-char/en

Internet

https://www.jstage.jst.go.jp/article/jamdsm/17/5/17_2023jamdsm0064/_pdf/-char/en

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