Int. J. Industrial and Systems Engineering, Vol. 25, No. 2, 2017 197 Copyright © 2017 Inderscience Enterprises Ltd. An intelligent approach for multi-response optimisation of WEDM parameters Bijaya Bijeta Nayak* and Siba Sankar Mahapatra Department of Mechanical Engineering, National Institute of Technology, Rourkela 769008, India Email: bijeta_bijaya@yahoo.co.in Email: mahapatrass2003@gmail.com *Corresponding author Abstract: Usually, multi-criteria decision making methods are embedded with design of experiment (DOE) approach for handling the multi-response optimisation problems. However, uncertainties, impreciseness and arbitrary human judgement for weight assignment to criteria and alternatives result in inferior solutions. To overcome this limitation, an intelligent approach based on neuro-fuzzy system is proposed for converting multi-responses into single equivalent response. To illustrate the superiority of the proposed approach, a complex case study of taper cutting operation using wire electrical discharge machining (WEDM) process is considered. The effect of process parameters on equivalent response has been studied in detail and the relationship between the input parameters and responses are established by means of a nonlinear regression analysis resulting in a valid mathematical model. Finally, optimal parameter setting is obtained by recently proposed meta-heuristics like bat algorithm. Keywords: multi-response performance characteristic index; MPCI; neuro-fuzzy system; taper cutting; angular error; bat algorithm. Reference to this paper should be made as follows: Nayak, B.B. and Mahapatra, S.S. (2017) ‘An intelligent approach for multi-response optimisation of WEDM parameters’, Int. J. Industrial and Systems Engineering, Vol. 25, No. 2, pp.197–227. Biographical notes: Bijaya Bijeta Nayak is a research student in the Department of Mechanical Engineering, National Institute of Technology Rourkela India. She obtained her BTech degree from Biju Patnaik University of Technology. She obtained her MTech degree from KIIT University, Odisha India. Currently, she is pursuing her doctoral work in the Department Mechanical Engineering at National Institute of Technology Rourkela. She has more than four years of experience in teaching. Her current area of research includes manufacturing processes, non-conventional machining processes, process modelling, and quality engineering. She has presented eight papers in international conferences. Siba Sankar Mahapatra is a Professor in the Department of Mechanical Engineering, National Institute of Technology Rourkela, India. He has more than 20 years of experience in teaching and research. His current area of research includes multi-criteria decision-making, quality engineering, assembly line balancing, group technology, neural networks, and non-traditional