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Selection of Optimal Cutting Parameters in Parallel Turnings Using Genetic Heuristics

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Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9771))

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Abstract

Optimal cutting parameters can lead to considerable savings in manufacturing fields. In this paper, to deal with the optimization problem of cutting parameters which aims to minimize the unit production cost (UC) in parallel turnings, we propose a novel optimization approach which divides this complicated problem into several sub-problems. Then a genetic algorithm (GA) is developed to search the optimal results for each sub-problem. Simulations show that the corresponding approach can find better results than previous approach to significantly reduce the production cost.

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Acknowledgments

This work was supported by the Natural Science Foundation of Fujian Province of China (No. 2016J01735, JK2015025), and the Foundation for Young Professors of Jimei University, China (No. 2011C002).

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Authors and Affiliations

  1. School of Science, Jimei University, Xiamen, 361021, China

    Lifang Pan

  2. School of Computer Engineering, Jimei University, Xiamen, 361021, China

    Shutong Xie & Jiangfu Liao

  3. School of Software, Xiamen University, Xiamen, 361005, China

    Kunhong Liu

Authors
  1. Lifang Pan
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  2. Shutong Xie
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  3. Kunhong Liu
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  4. Jiangfu Liao
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Corresponding author

Correspondence to Shutong Xie .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari , Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong , North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2016 Springer International Publishing Switzerland

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Pan, L., Xie, S., Liu, K., Liao, J. (2016). Selection of Optimal Cutting Parameters in Parallel Turnings Using Genetic Heuristics. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-42291-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42290-9

  • Online ISBN: 978-3-319-42291-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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