Multi-response optimization of Ni-based laser cladding via principal component analysis and grey relational analysis

Abstract

Laser cladding is an advanced technology used for surface modification, demonstrating great potential in repairing automobile panel dies and molds. However, identifying the appropriate cladding parameters for this complex process involving multiple factors and responses can be challenging. In this study, a multi-response optimization method based on Principal Component Analysis (PCA) and Grey Relational Analysis (GRA) was proposed using Taguchi analysis to identify optimal cladding parameters. The ANOVA analysis was employed to investigate the influence of key parameters on the comprehensive performance of the laser cladding layer (LCL). The results demonstrated a significant correlation between the responses, and the PCA-based GRA optimization method was found to be more scientific and reasonable. The optimal combination of laser power, scanning speed, powder feeding rate, and defocusing distance was determined to be 1.0 kW, 11 mm/s, 1.2 r/min, and 3 mm, respectively. The resulting laser cladding specimen showed significant improvements in aspect ratio, dilution rate, depth of heat-affected zone (HAZ), micro-hardness, and cladding efficiency, with a 34.10%, 26.35%, 21.14%, 5.49%, and 33.88% increase respectively, compared to the specimen produced using the original parameters. Additionally, the LCL specimen after optimization exhibited a more uniform microstructure distribution and improved wear resistance by 23.53%. The excellent wear resistance was mainly attributed to the uniformly refined grain composition, dispersed carbide (CrmCn) and boride (CrB) hard particles, and solid solutions (γ-Ni, γ-NiFe). In summary, this proposed optimization method for Ni60-based LCL is feasible and has yielded significant performance improvements.