An effective LSSVM-based approach for milling tool wear prediction

In order to realize real-time and precise monitoring of the tool wear in the milling process, this paper presents a tool wear predictive model based on the stacked multilayer denoising autoencoders (SMDAE) technique, the particle swarm optimization with an adaptive learning strategy (PSO-ALS), and the least squares support vector machine (LSSVM). Cutting force and vibration information are adopted as the monitoring signals. Three steps make up the unique feature extraction and fusion method: multi-domain features extraction, principal component analysis (PCA)-based dimension reduction, and SMDAE-based dimension increment. As a novel feature representation learning approach, the SMDAE technique is utilized to fuse the PCA-based fusion features to enrich the effective information by increasing the dimension, thus helping polish up the predictive performance of the proposed model. PSO-ALS is used to obtain the optimal parameters for LSSVM, simplifying the problem and increasing the population diversity. Twelve sets of milling experiments are conducted to demonstrate the reliable performance of the proposed model. The experimental results show that the presented model is superior to models such as PSO-LSSVM in predictive performance, and the SMDAE technique effectively improves the prediction accuracy of the established model. The findings of this paper offer theoretical guidelines for monitoring milling tool wear in real industrial situations.

Automated summary

This paper proposes a tool wear predictive model based on the stacked multilayer denoising autoencoders (SMDAE) technique, particle swarm optimization with an adaptive learning strategy (PSO-ALS), and least squares support vector machine (LSSVM) to achieve real-time and precise monitoring of tool wear in the milling process. Cutting force and vibration information are used as monitoring signals. The model employs unique feature extraction and fusion methods, including multi-domain features extraction, PCA-based dimension reduction, and SMDAE-based dimension increment. Experimental results demonstrate the superior predictive performance of the proposed model compared to PSO-LSSVM, and the effectiveness of the SMDAE technique in improving prediction accuracy.