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Abstract

This paper explores the quantitative assessment and characterization of tool wear phenomena in advanced manufacturing processes, employing a multifaceted approach encompassing traditional measurements, image processing, machine learning, and predictive modeling. The study emphasizes the intricate dynamics of tool wear and its direct impact on cutting tool performance, addressing challenges in real-time monitoring and optimization of machining operations. Traditional methods like VBmax measurement are juxtaposed with advanced techniques such as the improved conditional generative adversarial net with a high-quality optimization algorithm (CGAN-HQOA), efficient channel attention destruction and construction learning (ECADCL), and shape descriptors based on contour, moments, orientations, and texture. Artificial intelligence algorithms, including support vector machine (SVM), random forest (RF), decision tree (DT), and artificial neural network (ANN), are applied for tool condition monitoring. A novel wear stage division-based tool wear prediction method (WSDTWP) utilizing symmetrized dot pattern (SDP) and multi-covariance Gaussian process regression (MCGPR) is proposed for enhanced predictive accuracy. Results are presented through visualizations, including 3D surface plots, providing insights into the relationships between cutting conditions and various wear parameters. The discussion underscores the pivotal role of tool wear assessment in optimizing manufacturing efficiency, while the findings contribute to a holistic understanding of tool wear dynamics

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Last Page

79

References

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