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Chemical reaction optimization algorithm for machining parameter of abrasive water jet cutting

Author

Listed:
  • Neeraj Kumar Bhoi

    (PDPM Indian Institute of Information Technology Design and Manufacturing)

  • Harpreet Singh

    (PDPM Indian Institute of Information Technology Design and Manufacturing)

  • Saurabh Pratap

    (Indian Institute of Technology (IIT-BHU))

  • Pramod K. Jain

    (Indian Institute of Technology (IIT-BHU)
    Indian Institute of Technology)

Abstract

Abrasive water jet cutting is one of the most prominent technique for the cutting of wide range of materials. Selection of the input process parameter with optimized condition determines the productivity and process applicability. Present paper describes the nature inspired meta-heuristic chemical reaction optimization (CRO) algorithm for the selection of input process parameter for the most favorable material removal rate (MRR). In the present paper ductile material model for the MRR is considered by CRO for the solution approach. Five input variables namely water jet pressure, diameter of nozzle, feed rate of nozzle, mass flow rate of abrasive and mass flow rate of water were considered for the material removal rate in abrasive water jet machining. It was found that CRO algorithms delivers improved performance compare to different algorithms such as genetic algorithm, cuckoo search, teaching learning-based optimization and teaching learning based cuckoo search algorithm. The predicted results can be used for the identification of the input process parameter to enhance outcome at the acceptable range for machining.

Suggested Citation

  • Neeraj Kumar Bhoi & Harpreet Singh & Saurabh Pratap & Pramod K. Jain, 2022. "Chemical reaction optimization algorithm for machining parameter of abrasive water jet cutting," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 350-363, March.
    • Handle: RePEc:spr:opsear:v:59:y:2022:i:1:d:10.1007_s12597-021-00547-z
    DOI: 10.1007/s12597-021-00547-z
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    References listed on IDEAS

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    1. Nurezayana Zainal & Azlan Mohd Zain & Nor Haizan Mohamed Radzi & Muhamad Razib Othman, 2016. "Glowworm swarm optimization (GSO) for optimization of machining parameters," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 797-804, August.
    2. Mohamed Arezki Mellal & Edward J. Williams, 2016. "Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 927-942, October.
    3. Surajit Nath & Bijan Sarkar, 2018. "Decision system framework for performance evaluation of advanced manufacturing technology under fuzzy environment," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 703-720, November.
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    Cited by:

    1. N. Kailey & Sonali Sethi & Vivek Dhingra, 2024. "Correspondence between a new pair of nondifferentiable mixed dual vector programs and higher-order generalized convexity," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1507-1540, September.
    2. Mahmudul Hasan & Md. Rafiqul Islam & Amrita Ghosh Mugdha, 2023. "Solving maximum clique problem using chemical reaction optimization," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1230-1266, September.

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