Title: Multi-objective optimisation during drilling of CFRP composites: a PCA-fuzzy Taguchi integrated approach

Authors: Kumar Abhishek; Saurav Datta; Siba Sankar Mahapatra

Addresses: Department of Mechanical Engineering, National Institute of Technology, Rourkela-769008, India ' Department of Mechanical Engineering, National Institute of Technology, Rourkela-769008, India ' Department of Mechanical Engineering, National Institute of Technology, Rourkela-769008, India

Abstract: Drilling of carbon fibre reinforced plastics (CFRPs) is indeed very complicated due to the high structural stiffness of the composite as a whole and the low thermal conductivity of plastic matrix. During the drilling operation, the tool comes across matrix and reinforcement alternately which results in the destruction of fibre continuity with the generation of large stress concentration in the material and delamination at the hole during entry as well as exit. Hence, appropriate selection of drilling process parameters is indeed necessary for achieving efficient machining performance on drilling of CFRP composites. In order to achieve satisfactory product quality characteristics as well as excellent machining performance, the optimisation of machining parameters like drill bit diameter, spindle speed, and feed rate are deemed compulsory. The objective of this paper is to highlight the effect of aforesaid process parameters on thrust force, torque, surface roughness, entry delamination factor and exit delamination factor in the drilling of CFRP composites. The optimal setting of the controllable process parameters has been determined through experiments planned and conducted using the Taguchi's philosophy. The application of principal component analysis (PCA) combined with fuzzy inference system (FIS) and finally Taguchi's optimisation methodology in assessing of optimal parameter setting has been discussed in this article.

Keywords: carbon fibre reinforced plastic; CFRP; delamination; principal component analysis; PCA; Taguchi's philosophy; fuzzy inference system; FIS.

DOI: 10.1504/IJISE.2017.083672

International Journal of Industrial and Systems Engineering, 2017 Vol.26 No.2, pp.182 - 200

Received: 20 Sep 2014
Accepted: 21 May 2015
Published online: 19 Apr 2017 *

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