Modeling Material Machining Conditions with Gear-Shaper Cutters with TiN0.85-Ti in Adhesive Wear Dominance Using Machine Learning Methods
This paper examines the challenges of machining structural alloy steels for carburizing, with a particular focus on gear manufacturing. TiN0.85-Ti coatings were applied to cutting tool blades to improve machining quality and tool life. The research, supported by mathematical modeling, demonstrated t...
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| Published in: | Materials Vol. 17; no. 22; p. 5567 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
14-11-2024
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| Online Access: | Get full text |
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| Summary: | This paper examines the challenges of machining structural alloy steels for carburizing, with a particular focus on gear manufacturing. TiN0.85-Ti coatings were applied to cutting tool blades to improve machining quality and tool life. The research, supported by mathematical modeling, demonstrated that these coatings significantly reduce adhesive wear and improve blade life. The Kolmogorov–Arnold Network (KAN) was identified as the most effective model comprehensively describing tool life as a function of cutting speed, coating thickness, and feed rate. The results indicate that gear production efficiency can be significantly increased using TiN0.85-Ti coatings. |
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| ISSN: | 1996-1944 1996-1944 |
| DOI: | 10.3390/ma17225567 |