Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

[1]  Andrew Y. C. Nee,et al.  An Automated Process Planning System Based on Genetic Algorithm and Simulated Annealing , 2002 .

[2]  Ian Davidson,et al.  Clustering with Constraints , 2009, Encyclopedia of Database Systems.

[3]  Y W Guo,et al.  Operation sequencing optimization using a particle swarm optimization approach , 2006 .

[4]  L. Qiao,et al.  A GA-based approach to machining operation sequencing for prismatic parts , 2000 .

[5]  Zhang Wei Bo,et al.  Optimization of process route by Genetic Algorithms , 2006 .

[6]  Andrew Y. C. Nee,et al.  Optimization of process plans using a constraint-based tabu search approach , 2004 .

[7]  Andrew Y. C. Nee,et al.  Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts , 2002 .

[8]  Dimitris Kiritsis,et al.  Branch and fathoming algorithms for operation sequencing in process planning , 2001 .

[9]  Martina Gorges-Schleuter,et al.  ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy , 1989, ICGA.

[10]  Yunfeng Zhang,et al.  Process planning optimization for the manufacture of injection moulds using a genetic algorithm , 2003, Int. J. Comput. Integr. Manuf..

[11]  Lixin Tang,et al.  A modified genetic algorithm for the flow shop sequencing problem to minimize mean flow time , 2002, J. Intell. Manuf..

[12]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[13]  Leticia C. Cagnina,et al.  Particle swarm optimization for sequencing problems: a case study , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[14]  Paul G. Maropoulos,et al.  Integration of tool selection with design - Part 1. Feature creation and selection of operations and tools , 2000 .

[15]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[16]  K. Case,et al.  Feature-based representation for manufacturing planning , 2000 .

[17]  Mitsuo Gen,et al.  An effective coding approach for multiobjective integrated resource selection and operation sequences problem , 2006, J. Intell. Manuf..

[18]  S. V. Bhaskara Reddy.  Operation sequencing in CAPP using genetic algorithms , 1999 .

[19]  Mojtaba Salehi,et al.  Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning , 2009, Eng. Appl. Artif. Intell..

[20]  Lian Ding,et al.  Global optimization of a feature-based process sequence using GA and ANN techniques , 2005 .