An integrated multi-response optimisation route combining principal component analysis, fuzzy inference system, nonlinear regression and JAYA algorithm: a case experimental study on machining of GFRP (epoxy) composites

Kumar Abhishek, V. Rakesh Kumar, Saurav Datta and Siba Sankar Mahapatra

International Journal of Industrial and Systems Engineering, 2019, vol. 32, issue 4, 497-525

Abstract: Machining (drilling) operations have been performed on glass fibre reinforced polymer (GFRP) (epoxy) composites. The work intended to evaluate the most favourable setting of controllable process parameters which could simultaneously satisfy multi-requirements of process performance yield; in view of product quality as well as productivity. During drilling, three process parameters viz. drill rotational speed, feed rate and drill diameter have been considered to optimise thrust, torque and delamination factor (entry and exit both), simultaneously. Owing to the limitations of traditional Taguchi method-based optimisation approaches, the study proposes an integrated optimisation module combining principal component analysis (PCA), fuzzy inference system (FIS), nonlinear regression and JAYA algorithm towards optimising correlated multi-response features during machining of GFRP (epoxy) composites. JAYA is parameter (algorithm-specific)-less algorithm; which is used to solve constrained and unconstrained optimisation problems. Application potential of the aforesaid integrated optimisation route has been compared to that of teaching-learning-based optimisation (TLBO) algorithm; good agreement has been observed.

Keywords: glass fibre reinforced polymer; GFRP; Taguchi method; principal component analysis; PCA; fuzzy inference system; FIS; nonlinear regression; JAYA algorithm; teaching-learning-based optimisation algorithm; TLBO. (search for similar items in EconPapers)
Date: 2019
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