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.
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.
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Tuyboyov O.V. | PhD, Associate Professor, | Tashkent State Technical University |
| № | Havola nomi |
|---|---|
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