Computer Science ›› 2021, Vol. 48 ›› Issue (6): 175-183.doi: 10.11896/jsjkx.210100101

• Artificial Intelligence • Previous Articles     Next Articles

Intelligent Prediction Model of Tool Wear Based on Deep Signal Processing and Stacked-ResGRU

HU De-feng, ZHANG Chen-xi, WANG Shi-tao, ZHAO Qin-pei, LI Jiang-feng   

  1. School of Software Engineering,Tongji University,Shanghai 201804,China
  • Received:2021-01-13 Revised:2020-03-11 Online:2021-06-15 Published:2021-06-03
  • About author:HU De-feng,born in 1995,postgra-duate.Her main research interests include intelligent monitoring and tool wear prediction.(1831596@tongji.edu.cn)
    LI Jiang-feng,born in 1983,associate professor,is a member of China Computer Federation.His main research interests include artificial intelligence and blockchain.
  • Supported by:
    Natural Science Foundation of Shanghai (20ZR1460500).

Abstract: Intelligent monitoring of tool wear is an important factor affecting the intelligent development of modern machinery industry.Most machining tools collect signals through sensors and establishes their relationship to obtain the tool wear prediction without interrupting the processing process.Automatic tool change or alarm will be carried out according to whether the wear threshold is reached to realize online intelligent monitoring.It is an urgent problem to extract effective feature information from sensor signals and build a model which can predict the tool wear quickly and accurately.Therefore,based on the above problems,this paper proposes a tool wear prediction method based on deep signal processing and Stacked-ResGRU.In signal processing,the BiGRU-Self Attention(BGSA)network combing bidirectional GRU and self-attention is designed to obtain dynamic temporal features and reflect the influence degree of different features,which can improve modeling efficiency.At the same time,the Stacked-ResGRU model is proposed to predict the real-time tool wear value,and the network structure is optimized by residual structure to accelerate the convergence speed of the model.Experimental studies for tool wear prediction in a dry milling operation are conducted to demonstrate the viability of this method.Through the experimental results and comparative analysis,the proposed method can effectively characterize the tool wear degree,greatly reduce the prediction errors,and achieve a precise prediction effect.

Key words: Intelligent monitoring, Residual GRU, Self-attention, Signal processing, Tool wear

CLC Number: 

  • TP181
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