Optimisation of friction properties and grey correlation of laser-prepared ceramic microtextures

Abstract

Alumina ceramics possess broad application prospects in the grinding field owing to their excellent wear resistance and chemical stability. However, optimisation of their surface properties remains necessary to enhance grinding efficiency and workpiece quality. This study aims to investigate the effects of circular pit microtexture parameters (surface density, diameter, and depth) on the grinding performance of alumina ceramic grinding discs, and to conduct multi-variable optimisation of surface roughness (Ra), wear rate (w), and average friction coefficient (μ) during precision grinding. Firstly, microtextures were fabricated on the surface of alumina ceramics via laser processing. Secondly, a three-factor, three-level orthogonal experiment (varying surface density, diameter, and depth) was designed to analyse the influence laws of these parameters on the workpiece’s surface roughness, wear rate, and average friction coefficient. Finally, the grey relational analysis (GRA) method was adopted to convert the multi-objective optimisation problem of process parameters into a single-objective grey relational grade optimization problem, and the optimization results were validated through friction and wear tests. The results indicate that surface density exerts the most significant influence on surface roughness, diameter has the greatest impact on wear rate, and depth is the dominant factor governing the average friction coefficient. The optimised microtexture parameter combination obtained via GRA is a surface density of 15%, a diameter of 80 μm, and a depth of 70 μm. This parameter combination enables achieving low surface roughness and friction coefficient while maintaining a relatively high workpiece wear rate. These research findings hold important practical significance and research value for the manufacturing of high-performance grinding discs.