Abstract
The present research addresses the multi-criteria modelling and optimisation of electrical discharge machining (EDM) process, via optimised back propagation neural networks (OBPNN) and simulated annealing (SA) algorithm. The process response characteristics considered are material removal rate, surface roughness, and tool wear rate. The process input parameters include voltage, peak current, pulse off time, and pulse on time and duty factor. The three performance characteristics are combined into a single objective using weighted normalised grades (WNG) obtained from experimental study based on Taguchi method, to develop the artificial neural network (ANN) model. In order to enhance the prediction capability of the proposed model, its architecture is tuned by SA algorithm. Next, the developed model is embedded into SA algorithm to determine the best set of process parameters values for an optimal set of outputs. Experimental results indicate that the proposed optimisation procedure is quite efficient in modelling and optimisation of EDM process parameters. [Received 25 January 2015; Revised 12 April 2015; Accepted 3 May 2015]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
