Optimization of hole-making operations for injection mould using particle swarm optimization algorithm

Abstract: Optimization of hole-making operations plays a crucial role in which tool travel and tool switch scheduling are the two major issues. Industrial applications such as moulds, dies, engine block etc. consist of large number of holes having different diameters, depths and surface finish. This results into to a large number of machining operations like drilling, reaming or tapping to achieve the final size of individual hole. Optimal sequence of operations and associated cutting speeds, which reduce the overall processing cost of these hole-making operations are essential to reach desirable products. In order to achieve this, an attempt is made by developing an effective methodology. An example of the injection mould is considered to demonstrate the proposed approach. The optimization of this example is carried out using recently developed particle swarm optimization (PSO) algorithm. The results obtained using PSO are compared with those obtained using tabu search method. It is observed that results obtained using PSO are slightly better than those obtained using tabu search method.

How to cite this paper

 Dalavi, A., Pawar, P & Singh, T. (2015). Optimization of hole-making operations for injection mould using particle swarm optimization algorithm.International Journal of Industrial Engineering Computations , 6(4), 433-444.

Refrences

Alam, M. R., Lee, K. S., Rahman, M., & Zhang, Y. F. (2003). Process planning optimization for the manufacture of injection moulds using a genetic algorithm. International journal of computer integrated manufacturing, 16(3), 181-191.

Van Den Bergh, F. (2006). An analysis of particle swarm optimizers (Doctoral dissertation, University of Pretoria).

Van den Bergh, F., & Engelbrecht, A. P. (2006). A study of particle swarm optimization particle trajectories. Information sciences, 176(8), 937-971.

Bhongade A. S., & Khodke P.M. (2012) .Heuristics for production scheduling problem with machining and assembly operations. International Journal of Industrial Engineering Computations, 3, 185–198.

Carlos A.Coello Coello, Gary B.Lamont & David A. Van Veldhuizen (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, 2nd ed., 584-593.

Castelino, K., D’Souza, R., & Wright, P.K. (2002). Tool path optimization for minimizing airtime during machining. Journal of Manufacturing Systems, 22(3),173-180.

Chandramouli A., ArunVikram M. S. and Ramaraj N. (2012). Evolutionary approaches for scheduling a flexible manufacturing system with automated guided vehicles and robots. International Journal of Industrial Engineering Computations, 3, 627–648.

Dong, Y., Tang, J., Xu, B., & Wang, D. (2005). An application of swarm optimization to nonlinear programming. Computers & Mathematics with Applications, 49(11), 1655-1668.

Eberhart R.C. & Shi Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. In Proc. Congr. Evalutionary Computing, 84-89.

Elbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms. Advanced engineering informatics, 19 (1), 43-53.

Ghaiebi, H., & Solimanpur, M. (2007). An ant algorithm for optimization of hole-making operations. Computers & Industrial Engineering, 52(2), 308-319.

Guo, Y.W., Li, W.D., Mileham, A.R., & Owen, G.W. (2009). Applications of particle swarm optimisation in integrated process planning and scheduling. Robotics and Computer-Integrated Manufacturing, 25, 280–288.

Hsieh, Y. C., Lee, Y. C., & You, P. S. (2011). Using an effective immune based evolutionary approach for the optimal operation sequence of hole-making with multiple tools. Journal of Computational Information Systems, 7(2), 411-418.

Jiang, M., Luo, Y.P., Yang. S.Y. (2007).Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters, 102, 8–16.

Abu Qudeiri, J., Yamamoto, H., & Ramli, R. (2007). Optimization of operation sequence in CNC machine tools using genetic algorithm. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 1(2), 272-282.

Kennedy, J. & Eberhart. R. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942-1948.

Kolahan, F., & Liang, M. (2000). Optimization of hole-making operations: a tabu-search approach. International Journal of Machine Tools and Manufacture, 40 (12), 1735-1753.

Luong, L.H.S., & Spedding, T. (1995). An integrated system for process planning and cost estimation in hole-making. International Journal of Manufacturing Technology, 10, 411–415.

Merchant, R.L. (1985). World trends and prospects in manufacturing technology. International Journal for Vehicle Design, 6, 121–138.

Rao, R. V. (2011). Modeling and optimization of modern machining processes. In Advanced Modeling and Optimization of Manufacturing Processes (pp. 177-284). Springer London.

Shahsavari Pour N., R. Tavakkoli-Moghaddam & Asadi H. (2013).Optimizing a multi-objectives flow shop scheduling problem by a novel genetic algorithm. International Journal of Industrial Engineering Computations, 4, 345–354.

Shao, X., Li, X., Gao, L., & Zhang, C. (2009). Integration of process planning and scheduling—A modified genetic algorithm-based approach. Computers & Operations Research, 36, 2082 – 2096.

Shi, X.H., Liang, Y.C., Lee, H.P., Lu, C., & Wang. Q.X. (2007). Particle swarm optimization-based algorithms for TSP and generalized TSP. Information Processing Letters, 103, 169–176.

Tamjidy, M., Paslar, S., Baharudin, B. H. T., Hong, T. S., & Ariffin, M. K. A. (2014). Biogeography based optimization (BBO) algorithm to minimise non-productive time during hole-making process. International Journal of Production Research, 1-15.

Zhang, C., Sun, J., Zhu, X., Yang, Q. (2008). An improved particle swarm optimization algorithm for flow shop scheduling problem. Information Processing Letters, 108, 204–209.

Zhang, W-B., Zhu, G. (2011). Comparison and application of four versions of particle swarm optimization algorithms in the sequence optimization. Expert Systems with Applications, 38, 8858–8864.

Zhao, R. (1992). Handbook for machinists. Shanghai Science and Technology Press, China.