A hybrid optimization approach for setup planning with tolerance constraints

Abstract

Computer-aided process planning (CAPP) plays an important role in integrated manufacturing system and it can serve as a bridge between CAD and CAM. As a crucial part of CAPP, setup planning is a multi-constraint problem, in which the precision takes priority over efficiency. However, instead of precision constraints, traditional optimization methods have paid much more attention to efficiency requirements. This leads to the reduction in the precision of the final parts. This paper develops an optimization approach for solving computer-aided setup planning problem, which takes into account various constraints, especially the precision requirements specified by designers. First, objective function of the optimization model is formulated and a series of constraints, including feature precedence, tool approaching direction (TAD), and precision requirements are systematically created. Next, the model is solved by using a hybrid particle swarm optimization algorithm. In order to overcome the local optimum trap, mutation and exchange operations are adopted from the genetic algorithm. Finally, a part is tested in the case study and the validation of this method is proved.

Hybrid Particle Swarm Optimization Algorithm for Process Planning

“…Miljković et al [11] solve the multi-objective process planning problem which considering the indexes of minimize production cost and production time with an improved particle swarm optimization algorithm. Wu et al [12] designed a hybrid algorithm of particle swarm optimization and genetic algorithm to solve the process planning problem, and verified the effectiveness of the algorithm with an example. Ma et al [13] used the simulated annealing algorithm to solve the process planning.…”

“…Step 3: Randomly select an operation segment corresponding to one manufacturing feature, which has the flexible process, for logic layer mutation. In the example, the operation segment (11,12) belonging a feature is selected, and read the corresponding logic code (0,1); Step 4: Randomly select other optional process of the manufacturing feature to generate a new logic code, in the example (1,0); Step 5: Use the new logic code generated by Step 4 to replace the original logic code in O to obtain the offspring O after mutation.…”