刮擦
兴奋剂
材料科学
工程物理
光电子学
复合材料
工程类
作者
Ming Liu,Zhitong Xu,Noraphat Yuktanan,Tang Gu,Guangan Zhang,Jinyang Jiang,Fuqian Yang,Rui Liang
出处
期刊:Friction
[Springer Nature]
日期:2025-08-12
标识
DOI:10.26599/frict.2025.9441166
摘要
Understanding contact-induced damage is of paramount importance in the analysis of the lifespan and performance of surface coatings. In this work, we investigate the effects of dopants and interlayers on the structural durability of diamond-like carbon coatings (DLCs) and molybdenum disulfide (MoS2) coatings on stainless steel via micro-scratch tests. The analysis of XPS survey spectra and Raman spectra of DLCs shows that the ratio of sp2/sp3 (i.e., the intensity ratio of sp2 over sp3 obtained by XPS) is proportional to ID/IG, where ID and IG are the intensities of D and G bands of the Raman spectra. The analysis of the scratch tests reveals that there are three critical loads for the scratch-induced damage of the DLCs and MoS2 coatings, corresponding, respectively, to the initiation of periodic V-cracking, the minimum load for periodic semicircle cracking or peel-off, and the minimum load for partial and periodic delamination. Dopants can reduce the friction coefficient of the DLCs and have negligible effect on the Ti/MoS2 coatings. The Cr interlayer can better enhance the bonding strength between the DLCs and the steel substrate than the Si interlayer. Doping Cr and H can reduce the hardness of DLCs; doping Si can increase the hardness of the DLCs; and doping Ti, Pb, and PbTi can reduce the hardness of the MoS2 coatings. Deep Symbolic Optimization (DSO) algorithm is used to establish nominal-mathematical formulations between the critical variables for the scratch test and the materials parameters of the surface coating. The DSO analysis demonstrates the feasibility of using “deep-learning” to establish “quantitative” relationships between the critical variables for mechanical deformation and materials parameters.
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