Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder

The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a hea...

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Bibliographic Details
Published in:	International journal of advanced manufacturing technology Vol. 125; no. 9-10; pp. 4739 - 4752
Main Authors:	Lei, Yuncong, Li, Changgen, Guo, Liang, Gao, Hongli, Liang, Junhua, Sun, Yi, He, Jigang
Format:	Journal Article
Language:	English
Published:	London Springer London 01.04.2023 Springer Nature B.V
Subjects:	Adhesive wear Advanced manufacturing technologies CAE) and Design Classification Computer-Aided Engineering (CAD Decision trees Engineering Fault diagnosis Industrial and Production Engineering Manufacturing Mechanical Engineering Media Management Methods Milling cutters Original Article Representations Sensors Tool changing Wear Workpieces Milling cutters Online quantitative monitoring Convolutional autoencoder Fusion metric Health indicator
ISSN:	0268-3768, 1433-3015
Online Access:	Get full text
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