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 |
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14-11-2024
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| Abstract | 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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| AbstractList | 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. |
| Author | Józwik, Jerzy Tomiło, Paweł Kupczyk, Maciej Leleń, Michał |
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| Cites_doi | 10.1016/S0043-1648(01)00620-2 10.1088/1757-899X/1107/1/012114 10.1016/0040-6090(89)90030-8 10.1214/aos/1013203451 10.1016/j.cirpj.2016.11.004 10.1007/s00170-023-11681-x 10.1051/matecconf/201713900222 10.1201/b19559 10.3390/s20010227 10.1016/j.wear.2021.203737 10.1016/j.wear.2010.01.006 10.3390/nano13233039 10.1002/srin.196203379 10.1016/0043-1648(93)90360-X 10.1007/s00170-016-8704-3 10.1016/0043-1648(86)90106-7 10.26701/ems.352444 10.1016/j.procir.2018.08.237 10.3390/ma16196442 10.1007/s00170-022-10591-8 10.1007/s00170-021-06849-2 10.3390/ma13010123 |
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