Grey relational analysis-based composite coating property optimization fabricated by laser cladding

This paper aims at optimizing the surface hardness and wear resistance of composite coatings. The influence of Ni35A proportions in the composite mixing with TiC, WC, and W6Mo5Cr4V2 were explored based on mixed-level orthogonal experiment design. Experiments were conducted to examine the hardness and friction. The worn morphology was analysed by the white light interferometer. The multi-response optimization was achieved by the grey correlation analysis. The results show that both the powder ratio and powder type had significant impact on the micro-hardness and wear volume of the coatings, where powder ratio has more significance. Finally, the multi-index optimization was conducted with the target of maximizing the micro-hardness and minimizing the wear volume simultaneously by combining grey relational analysis and orthogonal experiment. The error rate is 1.122% between the prediction and experimental validation. Compared with the substrate, the micro-hardness of optimized cladding layer was improved from 15.2 to 68.8 HRC, while the wear volume was reduced from 15.28257 to 1.04831 center dot 10(-3) mm(3). The comparison of the optimal group in the orthogonal experiment indicates that the micro-hardness and wear resistance of optimized cladding layer were also improved. The optimized cladding layer has fine, dense, and evenly distributed grains with coarse and complete grain boundaries, suggesting that the micro-hardness and wear resistance were improved after optimization. The application of the mixed-level orthogonal experiment design combined with the grey correlation analysis allows to achieve the optimal selection of various hard-phase composite materials and the process optimization.