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

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 (L27)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\hbox {L}_{27})$$\end{document} 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.