Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions
Open Access
- 20 July 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 140726-140735
Abstract
Tool wear prediction is essential to ensure part quality and machining efficiency. Tool wear is affected by factors such as the material, structure, process, and processing time of the parts. Tool wear under the variable working conditions and the above factors show a complex coupling and timing correlation, which makes it challenging to predict tool wear under variable working conditions. This article aims to resolve this issue. First, we establish a unified representation of working condition factors. The stacked autoencoder (SAE) model adaptively extracts tool wear features from the machining signal. The extracted wear features and respective working conditions then combine into a working condition feature sequence for predicting tool wear. Finally, the advantages of the long short-term memory (LSTM) model to solve memory accumulation effects learn the regular wear pattern of the working condition feature sequence to realize the prediction of the tool wear. An experiment illustrates the effectiveness of the proposed method.Keywords
Funding Information
- National Key Research and Development Program of China (2019YFB1703800)
- Shanghai Aerospace Innovation Technology Fund program of China (SAST2018-063)
This publication has 19 references indexed in Scilit:
- Detection and control for tool wear based on neural network and genetic algorithmOptics and Precision Engineering, 2015
- Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief NetworksProcedia CIRP, 2015
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation2014
- New types of deep neural network learning for speech recognition and related applications: an overview2013
- Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference systemApplied Soft Computing, 2013
- On-line tool wear monitoring using geometric descriptors from digital imagesInternational Journal of Machine Tools and Manufacture, 2007
- ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCHMechanical Systems and Signal Processing, 2002
- Sensor signals for tool-wear monitoring in metal cutting operations—a review of methodsInternational Journal of Machine Tools and Manufacture, 2000
- Long Short-Term MemoryNeural Computation, 1997
- A review of machine vision sensors for tool condition monitoringComputers in Industry, 1997