A Spatial–Temporal Adaptive Graph Convolutional Network with Multi-Sensor Signals for Tool Wear Prediction

Tool wear monitoring is crucial for optimizing cutting performance, reducing costs, and improving production efficiency. Existing tool wear prediction models usually design integrated models based on a convolutional neural network (CNN) and recurrent neural network (RNN) to extract spatial and tempo...

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Vydáno v:Applied sciences Ročník 15; číslo 4; s. 2058
Hlavní autoři: Xia, Yu, Zheng, Guangji, Li, Ye, Liu, Hui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.02.2025
Témata:
Accuracy
Acoustics
attention mechanism
Data processing
Deep learning
Forecasts and trends
Liu Hui
Machine learning
multi-sensor fusion
Neural networks
Physics
Regression analysis
Sensors
Social networks
spatial–temporal graph neural network
tool wear prediction
Working conditions
ISSN:2076-3417, 2076-3417
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Shrnutí:Tool wear monitoring is crucial for optimizing cutting performance, reducing costs, and improving production efficiency. Existing tool wear prediction models usually design integrated models based on a convolutional neural network (CNN) and recurrent neural network (RNN) to extract spatial and temporal features separately. However, the topological structures between multi-sensor networks are ignored, and the ability to extract spatial features is limited. To overcome these limitations, a novel spatial–temporal adaptive graph convolutional network (STAGCN) is proposed to capture spatial–temporal dependencies with multi-sensor signals. First, a simple linear model is used to capture temporal patterns in individual time-series data. Second, a spatial–temporal layer composed of a bidirectional Mamba and an adaptive graph convolution is established to extract degradation features and reflect the dynamic degradation trend using an adaptive graph. Third, multi-scale triple linear attention (MTLA) is used to fuse the extracted multi-scale features across spatial, temporal, and channel dimensions, which can assign different weights adaptively to retain important information and weaken the influence of redundant features. Finally, the fused features are fed into a linear regression layer to estimate the tool wear. Experimental results conducted on the PHM2010 dataset demonstrate the effectiveness of the proposed STAGCN model, achieving a mean absolute error (MAE) of 3.40 μm and a root mean square error (RMSE) of 4.32 μm in the average results across three datasets.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15042058