Abstract
Aiming at the problems of low utilization rate of remanufacturing resources and high production cost in the process of remanufacturing assembly of used machinery, a new intelligent optimization slection matching approach for remanufacturing mechanical product assembly oriented to performance upgrading is proposed. Considering assembly accuracy, utilization rate of remanufacturing resources and production cost, Taguchi quality loss function and remanufacturing cost function are constructed, taking the closed-loop dimension chain as the constraint condition, an intelligent optimization model for remanufacturing of used mechanical parts is established. The optimization model is solved based on the combination of particle swarm optimization algorithm and genetic algorithm. Finally, taking CAK6140 machine tool spindle box as a case study to verify the feasibility and practicability of the proposed method, the spindle assembly process is optimized and matched. Compared the method proposed in this study with the grouping selection method often used by enterprises and the traditional particle swarm optimization model seeking method, constructed the corresponding evaluation index. The comparison results show that the comprehensive performance of the proposed method is the best. which proves that the method can effective solve the problem of low resource utilization rate, high cost and poor assembly accuracy in the used mechanical assembly process, and can achieve the goal of upgradeing the performance of mechanical equipment.