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A multi-attributive ideal-real comparative analysis-based approach for piston material selection

Author

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  • Saikat Chatterjee

    (Sikkim Manipal University)

  • Shankar Chakraborty

    (Jadavpur University)

Abstract

Piston is an essential component of an engine cylinder. High competition among the manufacturers urges them to explore the use of the best piston material in the engine cylinders. Materials ranging from aluminium to steel and iron are mostly utilized as the piston material. Forces from hot and expanding gases imposed inside the cylinder and many other critical factors make the selection of an appropriate piston material more demanding, since no single material satisfies all the required properties for a given application. In this paper, the performance of eight candidate piston materials is evaluated based on eight selection criteria. Entropy method is applied to estimate the criteria weights and multi-attributive ideal-real comparative analysis technique is adopted to identify the most suitable piston material. AISI 4140 steel emerges out as the top ranked piston material followed by AISI 8660 steel. A sensitivity analysis study is also performed to verify the consistency and robustness of the derived ranking results.

Suggested Citation

  • Saikat Chatterjee & Shankar Chakraborty, 2022. "A multi-attributive ideal-real comparative analysis-based approach for piston material selection," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 207-228, March.
    • Handle: RePEc:spr:opsear:v:59:y:2022:i:1:d:10.1007_s12597-021-00536-2
    DOI: 10.1007/s12597-021-00536-2
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    References listed on IDEAS

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    1. Kumar, Ravi & Singal, S.K., 2015. "Penstock material selection in small hydropower plants using MADM methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 240-255.
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    4. Morteza Yazdani & Ali Jahan & Ed. Kazimieras Zavadskas, 2017. "Analysis in Material Selection: Influence of Normalization Tools on COPRAS-G," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 59-74.
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    Cited by:

    1. Ivan Pavlenko & Ján Piteľ & Vitalii Ivanov & Kristina Berladir & Jana Mižáková & Vitalii Kolos & Justyna Trojanowska, 2022. "Using Regression Analysis for Automated Material Selection in Smart Manufacturing," Mathematics, MDPI, vol. 10(11), pages 1-16, May.

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