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2018 | 9 | nr 3 | 71--78
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Sequence Optimization of Hole-Making Operations for Injection Mould Using Shuffled Frog Leaping Algorithm with Modification

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Tool travel and tool switch planning are the two major issues in hole-making operations of industrial part which involves drilling, tapping etc. operations. It is necessary to find the sequence of operations, which minimizes the total non productive time and tool switch time of hole-making operations depending upon the hole location and the tool sequence to be followed. In this work, an attempt is made to reduce total non-productive time and tool switch time of hole-making operations by applying a relatively new algorithm known as shuffled frog leaping with modification for the determination of optimal sequence of operations. In order to validate the developed shuffled frog leaping algorithm with modification, it is applied on six different problems of holes and its obtained results are compared with dynamic programming (DP), ant colony algorithm (ACO), and immune based evolutionary approach (IA). In addition, an application example of injection mould is considered in this work to demonstrate the proposed approach. The result obtained by shuffled frog leaping algorithm with modification is compared with those obtained using ACO, particle swarm optimization (PSO) algorithm and IA. It is observed that the results obtained by shuffled frog leaping algorithm with modification are superior to those obtained using ACO, PSO and IA for the application example presented. (original abstract)
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  • Symbiosis International University, India
  • K. K. Wagh Institute of Engineering Education and Research, Nashik, India
  • Symbiosis International University, India
  • Kolahan F, Liang M., Optimisation of hole-making operations: a tabu-search approach, International Journal of Machine Tools & Manufacture, 40, 1735- 1753, 2000.
  • Merchant R.L., World trends and prospects in manufacturing technology, International Journal for Vehicle Design, 6, 121-138, 1985.
  • Luong L.H.S., Spedding T., An integrated system for process planning and cost estimation in holemaking, International Journal of Manufacturing Technology, 10, 411-415, 1995.
  • Alam M.R., Lee K.S., Rahman M. et al., Process planning optimisation for the manufacture of injection moulds using a genetic algorithm, International Journal of Computer Integrated Manufacturing, 16, 3, 181-191, 2003.
  • Qudeiri J.A., Hidehiko Y., Optimisation of operation sequence in CNC machine tools using genetic algorithm, Journal of Advanced Mechanical Design, Systems, and Manufacturing, 1, 2, 2007.
  • Ghaiebi H., Solimanpur M., An ant algorithm for optimisation of hole-making operations, Computers and Industrial Engineering, 52, 2, 308-319, 2007.
  • Hsieh Y.C., Lee Y.C., You P.S., 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, 2011.
  • Hsieh Y.C., Lee Y.C., You P.S., Chen T.C., Optimal operation sequence of hole-making with multiple tools in manufacturing: a PSO evolutionary based approach, Key Engineering Materials, Accepted, in press, 2010.
  • Tamjidy M., Shahla P., Biogeography based optimization (BBO) algorithm to minimize nonproductive time during hole-making process, Int. J. Production Research, 53, 6, 880-1894, 2015.
  • Adel T.A., Mohamed F.A., Karim H., Optimum drilling path planning for a rectangular matrix of holes using ant colony optimisation, International Journal of Production Research, 49, 19, 5877-5891, 2011.
  • Guo et al., Operation sequencing optimization using a particle swarm optimization approach, Proc IMechE Part B: Journal of Engineering Manufacture, 220, 12, 1945-1958, 2006.
  • Guo et al., Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach, Proc. IMechE Part B: Journal of Engineering Manufacture, 223, 5, 485-497, 2009.
  • Kiani K., Sharifi M., Shakeri M., Optimization of cutting trajectory to improve manufacturing time in computer numerical control machine using ant colony algorithm, Proc. IMechE Part B: Journal of Engineering Manufacture, 228, 7, 811-816, 2014.
  • Tarun Kumar Sharma, Millie Pant, Opposition based learning ingrained shuffled frog-leaping algorithm, Journal of Computational Science, 21, 307- 315, 2017.
  • Byrne G., Dimitrov D., Teti R., Houten F., Wertheim R., Biologicalisation: biological transformation in manufacturing, CIRP Journal of Manufacturing Sci. and Tech., 21, 1-32, 2018.
  • Wei-Bo Zhang, Guang-Yu Zhu, Drilling path optimization by optimal foraging algorithm, IEEE Transactions on Industrial Informatics, 14, 7, 2018.
  • Dewil R., Kü ¸çükğlu İ., Luteyn C. et al., A critical review of multi-hole drilling path optimization, Arch. Computat. Methods Eng., 2018,
  • Venkata Rao R., Modeling and optimization of Modern Machining processes, Springer Series in Advanced Manufacturing, 2011.
  • Elbeltagi E., Tarek H., Donal G., Comparison among five evolutionary based optimisation algo rithms, Advanced Engineering Informatics, 19, 43- 53, 2005.
  • Ammu P.K., Sivakumar K.C., Rejimoan R., Biogeography - based optimization - a survey, International Journal of Electronics and Computer Science Engineering, 2, 1, 154-160, 2012.
  • Eusuff M.M., Lansey K.E., Pasha F., Shuffled frogleaping algorithm: a memetic metaheuristic for discrete optimisation, Eng. Optim., 38, 2, 129-154, 2006.
  • Elbeltagi E., Tarek H., Donald G., A modified shuffled frog-leaping optimization algorithm: applications to project management, Structure and Infrastructure Engineering, 3, 1, 53-60, 2007.
  • Luo Ping, Qinang Lu, Chenxi Wu, Modified shuffled frog leaping algorithm based on new searching strategy, Seventh International Conference on Natural Computation, 2011.
  • Roy P., Roy Pritam, Chakrabarti A., Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect, Applied Soft Computing, 13, 4244-4252, 2013.
  • Niknam T., Narimani M.R., Jabbari M., Malekpour A.R., A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy, 36, 6420-6432, 2011.
  • Luo X.H., Yang Y., Li X., Solving TSP with shuffled frog-leaping algorithm, Proc. ISDA, 3, 228-232, 2008.
  • Kennedy J., Eberhart R.C., Particle swarm optimisation, Proc. IEEE Conf. Neural Network, 4, 1942- 1948, 1995.
  • Onwubolu G.C., Clerc M., Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimisation, International Journal of Production Research, 42, 3, 473- 491, 2004.
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