Multiobjective Optimization of Copper Coil Blanking Based on Niche Genetic Algorithm

Abstract

The shear process of small batch and multivariety copper strip processing enterprises are the bottleneck of production, which often faces the contradiction between working efficiency (less tool changing) and yield (reducing geometric waste). The high yield of copper strip embryo is the core index of high yield and high benefit for enterprise. Less tool changing is a key step for high efficiency and fast product delivery in shear station. In this paper, we took the cutting production of the production management system as a research example in Hubei Lean High Precision Copper Strip Company when the system is developing. We used the penalty function to deal with the length floating constraint. Then, we established a multiobjective optimization model with the roll weight and the number of tool changing as the weights, which were calculated by an integrated weighting method. Three algorithms, namely, adaptive particle swarm optimization, niche genetic algorithm based on crowding, and niche genetic algorithm based on seed retention (NGA), were used to solve the problem. Through production examples, it was concluded that the solution solved by NGA has the highest utilization rate of the coil when the number of tool changing was as little as possible. This paper provides a new solution combining the efficiency and benefit for shear process in finished product delivery of copper strip processing enterprises.