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Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach

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Abstract

The aim of this paper is to process modelling of AWJM process on machining of green composites using fuzzy logic (FL). An integrated expert system comprising of Takagi–Sugeno–Kang (TSK) fuzzy model with subtractive clustering (SC) has been developed for prediction surface roughness in green AWJM. Initially, the data base is generated by performing the experiments on AWJM process using Taguchi \((\hbox {L}_{27})\) orthogonal array. Thereafter, SC is used to extracts the cluster information which are then utilized to construct the TSK model that best fit the data using minimum rules. The performance of TSK–FL model has been tested for its accuracy in prediction of surface roughness in AWJM process using artificially generated test cases. The result shows that, predictions through TSK–FL model are comparable with experimental results. The developed model can be used as systematic approach for prediction of surface roughness in green manufacturing processes.

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Acknowledgments

The author would like to thanks Mr. Vijay Lagad, Managing Director allowing permission for carry out experiments and to utilize his valuable resources at a Supernova Waterjet Cutting Systems, Nashik. The author also thanks to Prof. N.V. Deshpande, Director NIT Silchar for his continuous encouragement towards the research and all support at NIT Silchar and Prof. Prashant Badgujar, Assistant Professor, Department of Mechanical Engineering, Institute of Technology-Polytechnic, Nashik, for his valuable helps during experimentation at Nashik.

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Authors and Affiliations

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

    Jagadish & Sumit Bhowmik

  2. Department of Training and placement, Jalpaiguri Government Engineering College, Jalpaiguri, West-Bengal, 735102, India

    Amitava Ray

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Jagadish, Bhowmik, S. & Ray, A. Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach. J Intell Manuf 30, 2965–2979 (2019). https://doi.org/10.1007/s10845-015-1169-7

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