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Prediction of WEDM Performances Using Clustering Techniques in ANFIS During Machining of A286 Superalloy

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Abstract

Wire Electric Discharge Machining (WEDM) involves a high degree of nonlinearity and stochastic phenomena due to its complexity and process anisotropy. Thus, prediction of WEDM performances must be carried out through an efficient tool. The choice of data clustering technique in adaptive neuro-fuzzy inference system (ANFIS) is crucial since it has a substantial impact on prediction accuracy. To this end, this study investigates the effect of the choice of clustering algorithms [grid partitioning (GP) and subtractive clustering (SC)] on the performance of ANFIS while forecasting the WEDM performances such as material removal rate and surface roughness. Sensitivity analysis is carried out with the analysis of variance test. The predictive capability of the ANFIS-GP model is found to be superior to the ANFIS-SC model. The percentage of error plots are showcased to check the efficacy of the selected ANFIS-GP model for the responses. The parametric studies are conducted to portray the effect of process variables on responses.

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Acknowledgements

This study acknowledges the Govt. of India, Ministry of Human Resource and Development for providing scholarship during the research period.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

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

    Subhankar Saha, Saikat Ranjan Maity & Sudip Dey

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  1. Subhankar Saha
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  2. Saikat Ranjan Maity
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  3. Sudip Dey
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Correspondence to Saikat Ranjan Maity.

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Saha, S., Maity, S.R. & Dey, S. Prediction of WEDM Performances Using Clustering Techniques in ANFIS During Machining of A286 Superalloy. J. Inst. Eng. India Ser. C 104, 315–326 (2023). https://doi.org/10.1007/s40032-023-00922-3

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