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An improved deep learning model for online tool condition monitoring using output power signals

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journal contribution
posted on 26.03.2021, 12:04 authored by Lang Dai, Tianyu Liu, Zhongyong Liu, Lisa JacksonLisa Jackson, Paul GoodallPaul Goodall, Changqing Shen, Lei Mao
Something like normal functionality of tools in a manufacturing process is typically designed to ensure reliability, where fast and accurate identification of tool abnormal operation plays a vital role in intelligent manufacturing. In this study, a novel method is proposed to assess the cutting tool condition, which consists of a convolutional neural network with wider first-layer kernels (W-CONV), and long short-term memory (LSTM). The analysis benefits from the use of output power signals from the cutting tool, since they can be obtained easily and efficiently, enabling the proposed method to be applicable in practical operation for online condition monitoring. Moreover, effectiveness of the proposed method is investigated, using test data from cutting tools at various tool wear conditions. Results demonstrate that with the proposed method, tool wear condition can be identified accurately and efficiently. Furthermore, with test data collected at cutting tools with different sizes, the robustness of the proposed method can be further clarified.

Funding

National Natural Science Foundation of China (NSFC) (51975549)

Anhui Provincial Natural Science Foundation (1908085ME161)

State Key Laboratory of Mechanical System and Vibration (MSV202017)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering
  • Mechanical, Electrical and Manufacturing Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Shock and Vibration

Volume

2020

Publisher

Hindawi

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Hindawi under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

10/11/2020

Publication date

2020-11-23

Copyright date

2020

ISSN

1070-9622

eISSN

1875-9203

Language

en

Depositor

Prof Lisa Jackson. Deposit date: 22 March 2021

Article number

8843314