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http://dx.doi.org/10.9725/kts.2020.36.6.320

Modification of Thin Film Friction and Wear Models with Effective Hardness  

Kim, Chang-Lae (Dept. of Mechanical Engineering, Chosun University)
Kim, Hae-Jin (Dept. of Mechanical and Aerospace Engineering, Gyeongsang National University)
Publication Information
Tribology and Lubricants / v.36, no.6, 2020 , pp. 320-323 More about this Journal
Abstract
Thin film coatings are commonly exploited to minimize wear and optimize the frictional behavior of various precision mechanical systems. The enhancement of thin film durability is directly related to the performance maximization of the system. Therefore, a fine approach to analyze the thin film wear behavior is required. Archard's equation is a representative and well-developed law that defines the wear coefficient, which is the probability of creating wear particles. A ploughing model is a commonly used model to determine the friction force during the abrasive contact. The equations demonstrate that the friction force and wear coefficient are inversely proportional to the hardness of the material. In this study, Archard's equation and ploughing models are modified with an effective hardness to minimize the gap between the experimental and numerical results. It is noted that the effective hardness is the hardness variation with respect to the penetration depth owing to the substrate effect. The nanoindentation method is utilized to characterize the effective hardness of Cu film. The wear coefficient value considering the effective hardness is more than three times higher than that without considering the effective hardness. The friction force predicted with the effective hardness agreed better with the results obtained directly from the friction force detecting sensor. This outcome is expected to improve the accuracy of friction and wear amount predictions.
Keywords
archard's wear model; ploughing friction model; elastic modulus; hardness; nanoindentation;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
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