Metaheuristics Algorithm-Based Optimization for High Conductivity and Hardness CuNi₂Si₁ Alloy

The optimization of CuNi₂Si₁ alloy's mechanical and electrical properties was achieved through a combination of experimental approaches and metaheuristic algorithms. Optimizing hardness and electrical conductivity through a variation in aging temperature (450-600 °C) and aging duration (1-420 min) was taken under consideration in the present work. Cold rolling with 50% strain after solution annealing aided in microstructure refinement and accelerated Ni₂Si precipitates' development, and property improvement increased. Optimum temperature and holding period were 450 °C and 30 min, respectively, with 266 HV and 13 MS/m and 167 HV and 11.2 MS/m for non-deformed samples, respectively. SPBO, genetic algorithm (GA), and particle swarm optimization (PSO) metaheuristic algorithms were considered, and SPBO exhibited the best prediction accuracy. SPBO predicted 450 °C for 61.75 min, and experimental testing exhibited 267 HV and 14 MS/m, respectively. Polynomial regressions with 0.98 and 0.96 values for R² confirmed these values' accuracy. According to this work, computational optimization proves effective in optimizing development and property tailoring for application in industries including aerospace and electrical engineering.