Effective Application of Neural Networks in Tool Condition Identification

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概要

• 論文の詳細を見る

• Neural networks (NNs) are widely used in intelligent tool condition monitoring (TCM) system, however, its effective application is still an ongoing research issue. This paper investigates some problems related with this topic, such as the selection of monitoring signals and signal feature set, and construction of performance evaluation function in TCM decision making, so as to realize reliable tool condition identification. Firstly, sensing signal selection and feature set selection are investigated. The former aims to choose the proper monitoring signals in terms of the above mentioned requirements. And the latter is to comprehensively take this monitoring signal features and identify the most effective set so as to give robust and reliable tool condition identification. From the viewpoint of reducing manufacturing cost, an evaluation function with the machining loss consideration is proposed as criteria in decision making. Besides, two related problems in TCM system design are discussed, multiclassification of tool states and hybrid system. Acoustic emission (AE) signals and cutting force signals from titanium machining, and AE signals from steel machining are used in this study.

• 一般社団法人日本機械学会の論文
• 2005-10-18

著者

• WONG Y.S.

  Department of Mechanical Engineering, National University of Singapore

• Wong Y.s.

  Department Of Mechanical Engineering National University Of Singapore

• SUN Y.

  Department of Chemical Engineering, Hanyang University

• Rahman M.

  Department Of Bioresource Science Ibaraki University College Of Agriculture

• Sun J.

  Department of Mechanical Engineering, National University of Singapore

• Hong G.S.

  Department of Mechanical Engineering, National University of Singapore

• Hong G.s.

  Department Of Mechanical Engineering National University Of Singapore

• Hong G.s.

  Department Of Mechanical And Production Engineering National University Of Singapore

• Sun Y.

  Department Of Adaptive Machine System Graduate School Of Engineering Osaka University

• Rahman M.

  Department Of Applied Chemistry And Biochemistry Kumamoto University

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