IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v59y2022i1d10.1007_s12597-021-00547-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-021-00547-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12597-021-00547-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. N. A. Fountas & R. Benhadj-Djilali & C. I. Stergiou & N. M. Vaxevanidis, 2019. "An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1581-1599, April.
    2. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    3. Mellal, Mohamed Arezki & Zio, Enrico, 2020. "System reliability-redundancy optimization with cold-standby strategy by an enhanced nest cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. Chidozie Chukwuemeka Nwobi-Okoye, 2020. "Modelling the performance of single-input–single-output (SISO) processes using transfer function and fuzzy logic," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 815-836, September.
    5. Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
    6. Maciej Grzenda & Andres Bustillo, 2019. "Semi-supervised roughness prediction with partly unlabeled vibration data streams," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 933-945, February.
    7. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    8. Esther Jose & Puneet Agarwal & Jun Zhuang & Jose Swaminathan, 2023. "A multi-criteria decision making approach to evaluating the performance of Indian railway zones," Annals of Operations Research, Springer, vol. 325(2), pages 1133-1168, June.
    9. Yongmao Xiao & Wei Yan & Ruping Wang & Zhigang Jiang & Ying Liu, 2021. "Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    10. Mohamed Arezki Mellal & Enrico Zio, 2019. "An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies," Journal of Risk and Reliability, , vol. 233(6), pages 1099-1105, December.
    11. Elango Natarajan & Varadaraju Kaviarasan & Wei Hong Lim & Sew Sun Tiang & S. Parasuraman & Sangeetha Elango, 2020. "Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 911-935, April.
    12. Byeongwoo Jeon & Joo-Sung Yoon & Jumyung Um & Suk-Hwan Suh, 2020. "The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1837-1859, December.
    13. Uoseph Hamdi Salemi & Esmaile Khorram & Yuancheng Si & Saralees Nadarajah, 2020. "Sensitivity analysis of censoring schemes in progressively type-II right censored order statistics," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 163-189, March.
    14. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.
    15. R. Venkata Rao & Dhiraj P. Rai & J. Balic, 2019. "Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2101-2127, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:opsear:v:59:y:2022:i:1:d:10.1007_s12597-021-00547-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.