Modeling Material Machining Conditions with Gear-Shaper Cutters with TiN0.85-Ti in Adhesive Wear Dominance Using Machine Learning Methods

被引：0

作者：

Kupczyk, Maciej ^{[1
]}

Lelen, Michal ^{[2
]}

Jozwik, Jerzy ^{[2
]}

Tomilo, Pawel ^{[3
]}

机构：

[1] Poznan Univ Tech, Inst Mech Technol, Fac Mech Engn, 3 Piotrowo St, PL-60965 Poznan, Poland

[2] Lublin Univ Technol, Fac Mech Engn, 36 Nadbystrzycka St, PL-20618 Lublin, Poland

[3] Lublin Univ Technol, Fac Management, 38 Nadbystrzycka St, PL-20618 Lublin, Poland

来源：

MATERIALS | 2024年 / 17卷 / 22期

关键词：

machining; carburizing steels; gear production; adhesive wear; titanium nitride coatings; tool durability; reactive pulse plasma method; Kolmogorov-Arnold Network; predictive modeling; cutting tools; HIGH-SPEED STEEL; MECHANISMS; LIFE; TOOL;

D O I：

10.3390/ma17225567

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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.

引用

页数：22