| Title |
Tool wear prediction using multi-domain features and quantised attention embedded CNN |
| Authors |
Murtaza, Aitzaz Ahmed ; Zafar, Muhammad Hamza ; Moosavi, Syed Kumayl Raza ; Aftab, Muhammad Faisal ; Sanfilippo, Filippo |
| DOI |
10.1016/j.measen.2026.102013 |
| Full Text |
|
| Is Part of |
Measurement: Sensors.. London : Elsevier. 2026, vol. 47, art. no. 102013, p. 1-15.. eISSN 2665-9174 |
| Keywords [eng] |
Multi-CNN attention ; Predictive analytics ; Quantisation ; Time–frequency domain ; Tool wear prediction |
| Abstract [eng] |
In precision manufacturing, accurate prediction of tool wear is pivotal for maintaining production quality and efficiency. This paper introduces a novel predictive model that employs a multi-branch convolutional neural network (multi-CNN) enhanced by an attention mechanism and Bayesian optimisation to forecast tool wear with high accuracy. The proposed approach is evaluated using the publicly available dataset, which comprises force and torque measurements acquired from machining processes. By incorporating time-domain (TD), frequency-domain (FD), and discrete wavelet transform (DWT) features, the model comprehensively analyses signals from machining processes, enabling robust predictions. The attention mechanism strategically emphasises significant features, augmenting the model’s predictive capability. Demonstrated results show superior performance with a mean absolute error (MAE) of 2.8, root mean squared error (RMSE) of 3.4, and an R2 score of 0.986, significantly outperforming conventional models. Additionally, post-training quantisation optimises the model for deployment on edge computing devices, maintaining effectiveness while being lightweight, ideal for real-time applications in resource-constrained environments. This study exemplifies how advanced machine learning techniques can improve predictive maintenance in manufacturing which not only enhances operational efficiency but also reduces downtime. |
| Published |
London : Elsevier |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
