Citation: Li, R.; Ye, X.; Yang, F.; Du,
K.-L. ConvLSTM-Att: An Attention-
Based Composite Deep Neural
Network for Tool Wear Prediction.
Machines 2023, 11, 297. https://
doi.org/10.3390/machines11020297
Academic Editor: Mark J. Jackson
Received: 17 January 2023
Revised: 12 February 2023
Accepted: 15 February 2023
Published: 16 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
ConvLSTM-Att: An Attention-Based Composite Deep Neural
Network for Tool Wear Prediction
Renwang Li
1,
*, Xiaolei Ye
1
, Fangqing Yang
1
and Ke-Lin Du
2
1
School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
2
Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
* Correspondence: renwangli@zstu.edu.cn
Abstract:
In order to improve the accuracy of tool wear prediction, an attention-based composite
neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed.
Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional
convolutional neural network (1D-CNN), which avoids the loss of wear features caused by manual
feature extraction. Then the temporal relationship learning between multidimensional feature vectors
is performed by introducing a long short-term memory (LSTM) network to make up for the lack of
long-short distance dependence of the captured sequence of the CNN network. Finally, an attention
mechanism is applied to strengthen the ability to extract key information from tool-wearing temporal
features. The proposed ConvLSTM-Att model is trained with the measured tool wear data and then
performs as a tool wear predictor. The model is compared with several state-of-the-art models on
the PHM tool wear data sets. It significantly outperforms the other models in terms of prediction
accuracy, but with similar computational complexity.
Keywords: tool wear prediction; feature extraction; attention; LSTM; metrology
1. Introduction
As a result of China’s vigorous promotion of the Manufacturing Industry 2025, the ma-
chinery manufacturing industry has increasingly higher requirements for intelligence
[1,2]
.
As an important part of machinery production and processing, the degree of wear and
tear on tools severely affects the accuracy of workpieces and the manufacturing costs
of enterprises. Most of the traditional tool changing methods are based on experience,
determining the timing of tool stopping and tool changing. Changing a tool too early will
cause wastage of the tool [
3
], whereas changing a tool too late will reduce the quality of
a workpiece and lead to scrapping. During the machining process, timely and accurate
prediction of tool wear is beneficial to both improving the machining accuracy of products
and reducing the manufacturing and labor costs of enterprises. Therefore, intelligent and
accurate prediction of tool wear has become an important topic.
Direct and indirect measurements are the two main approaches to tool wear pre-
diction [
4
]. The direct measurement approach requires off-line measurement of the tool
between machining intervals, which greatly affects the coherence of machining and is
difficult to apply in production practice. The indirect measurement method is primarily
used to predict tool wear by mining and analyzing the relationship between the data taken
during machining and the tool wear data. However, the data acquired during machining is
subject to noise in the industrial environment, which reduces the validity of the data [5].
Traditional machine learning approaches, such as artificial neural networks [
6
], fuzzy
logic [
7
], the hidden Markov model and support vector machine, as well as metaheuristic
approaches [
8
,
9
], can be implemented for tool wear prediction, but the prediction accuracy
is generally low [
10
]. It is difficult for traditional machine learning approaches to predict
the true data directly from the measured data [
11
]. Therefore, some preprocessing methods
Machines 2023, 11, 297. https://doi.org/10.3390/machines11020297 https://www.mdpi.com/journal/machines
