Transfer learning enabled convolutional neural networks for estimating health state of cutting tools

Mohamed Marei, Shirine El Zaatari, Weidong Li

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)
68 Downloads (Pure)

Abstract

Effective Prognostics and Health Management (PHM) for cutting tools during Computerized Numerical Control (CNC) processes can significantly reduce downtime and decrease losses throughout manufacturing processes. In recent years, deep learning algorithms have demonstrated great potentials for PHM. However, the algorithms are still hindered by the challenge of the limited amount data available in practical manufacturing situations for effective algorithm training. To address this issue, in this research, a transfer learning enabled Convolutional Neural Networks (CNNs) approach is developed to predict the health state of cutting tools. With the integration of a transfer learning strategy, CNNs can effectively perform tool health state prediction based on a modest number of the relevant images of cutting tools. Quantitative benchmarks and analyses on the performance of the developed approach based on six typical CNNs models using several optimization techniques were conducted. The results indicated the suitability of the developed approach, particularly using ResNet-18, for estimating the wear width of cutting tools. Therefore, by exploiting the integrated design of CNNs and transfer learning, viable PHM strategies for cutting tools can be established to support practical CNC machining applications.

Original languageEnglish
Article number102145
Number of pages11
JournalRobotics and Computer-Integrated Manufacturing
Volume71
Early online date2 Mar 2021
DOIs
Publication statusPublished - Oct 2021

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

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This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Funder

Coventry University , Unipart Powertrain Application Ltd. (U.K.), Institute of Digital Engineering (U.K.), and the National Natural Science Foundation of China (Project no. 51975444 ).

Keywords

  • Prognostics and Health Management
  • Transfer Learning
  • convolutional neural networks (CNN)
  • CNC

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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