scholarly journals Tool wear prediction based on multidomain feature fusion by attention-based depth-wise separable convolutional neural network in manufacturing

2021 ◽  
Author(s):  
Guofa Li ◽  
Yanbo Wang ◽  
Jili Wang ◽  
Jialong He ◽  
Yongchao Huo
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Wear Prediction ◽  
Tool Wear Prediction

scholarly journals Tool Wear Prediction Based on Multidomain Feature Fusion by Attention-Based Depth-Wise Separable Convolutional Neural Network in Manufacturing

2021 ◽  
Author(s):  
Guofa Li ◽  
Yanbo Wang ◽  
Jialong He ◽  
Yongchao Huo
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Prediction Accuracy ◽  
Feature Fusion ◽  
Prediction Method ◽  
Wear Prediction ◽  
Time Frequency ◽  
Tool Wear Prediction

Abstract Tool wear during machining has a great influence on the quality of machined surface and dimensional accuracy. Tool wear monitoring is extremely important to improve machining efficiency and workpiece quality. Multidomain features (time domain, frequency domain and time-frequency domain) can accurately characterise the degree of tool wear. However, manual feature fusion is time consuming and prevents the improvement of monitoring accuracy. A new tool wear prediction method based on multidomain feature fusion by attention-based depth-wise separable convolutional neural network is proposed to solve these problems. In this method, multidomain features of cutting force and vibration signals are extracted and recombined into feature tensors. The proposed hypercomplex position encoding and high dimensional self-attention mechanism are used to calculate the new representation of input feature tensor, which emphasizes the tool wear sensitive information and suppresses large area background noise. The designed depth-wise separable convolutional neural network is used to adaptively extract high-level features that can characterize tool wear from the new representation, and the tool wear is predicted automatically. The proposed method is verified on three sets of tool run-to-failure data sets of three-flute ball nose cemented carbide tool in machining centre. Experimental results show that the prediction accuracy of the proposed method is remarkably higher than other state-of-art methods. Therefore, the proposed tool wear prediction method is beneficial to improve the prediction accuracy and provide effective guidance for decision making in processing.

TOOL WEAR PREDICTION FROM ACOUSTIC EMISSION AND SURFACE CHARACTERISTICS VIA AN ARTIFICIAL NEURAL NETWORK

1999 ◽  
Vol 13 (6) ◽  
pp. 955-966 ◽  
Author(s):  
P. WILKINSON ◽  
R.L. REUBEN ◽  
J.D.C. JONES ◽  
J.S. BARTON ◽  
D.P. HAND ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Characteristics ◽  
Wear Prediction ◽  
Tool Wear Prediction ◽  
Artificial Neural
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