Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

Zhiwen Huang, Jianmin Zhu (), Jingtao Lei, Xiaoru Li and Fengqing Tian
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Zhiwen Huang: University of Shanghai for Science and Technology
Jianmin Zhu: University of Shanghai for Science and Technology
Jingtao Lei: Shanghai University
Xiaoru Li: University of Shanghai for Science and Technology
Fengqing Tian: University of Shanghai for Science and Technology

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 10, 953-966

Abstract: Abstract Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods.

Keywords: Tool wear predicting; Multi-domain; Feature fusion; Convolutional neural network; Milling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10845-019-01488-7

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