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Design of Multi-material Model for Wire Electro-discharge Machining of SS304 and SS316 Using Machine Learning and MCDM Techniques

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Abstract

Machine learning and optimisation of wire electro-discharge machining process are prevalent in single-material systems, but rare in multi-material systems, particularly in stainless steel, due to the large number of variables affecting responses. In the available literature, the design of such a machine learning approach is likewise not thoroughly described. The current study was conducted to identify the efficient multi-material model incorporating six process variables and seven compositional variables using several methods, such as response surface methodology, single-output multi-layer (SOML), and multi-output multi-layer (MOML) neural networks, to understand the response behaviours such as material removal rate, kerf width, over cut, and surface roughness (Ra) of SS304 and SS316 materials. Both SOML and MOML models have been found to be capable of predicting responses with good accuracy; however, SOML models are slightly more accurate. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used in conjunction with the SOML model, where the predicted responses of SOML models were used directly as inputs for process variable optimisation. The TOPSIS-generated optimum process variables for the multi-material system were validated, with each response having a prediction error of less than 10%.

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Acknowledgements

The authors would like to acknowledge the Department of Mechanical Engineering, NIT Silchar, Assam, India, and CSIR-CMERI, Durgapur, West Bengal, India, for providing the guidance, opportunity, and facilities to carry out the research work.

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This article is not received any grant from any funding agency in the public, commercial, or others.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Silchar, Assam, 788010, India

    Shatarupa Biswas & Yogesh Singh

  2. CSIR-Central Mechanical Engineering Research Institute, Durgapur, West Bengal, 713209, India

    Manidipto Mukherjee, Swapan Barman & Manivannan Raja

  3. SRM Institute of Science and Technology, SRM University, Chennai, Tamil Nadu, 603203, India

    Shubhabrata Datta

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  1. Shatarupa Biswas
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  2. Yogesh Singh
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Correspondence to Manidipto Mukherjee.

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Biswas, S., Singh, Y., Mukherjee, M. et al. Design of Multi-material Model for Wire Electro-discharge Machining of SS304 and SS316 Using Machine Learning and MCDM Techniques. Arab J Sci Eng 47, 15755–15778 (2022). https://doi.org/10.1007/s13369-022-06757-x

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