{'fraction_non_alphanumeric': 0.024237685691946835, 'fraction_numerical': 0.013291634089132134, 'mean_word_length': 5.7368421052631575, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An optimised back propagation neural network approach and simulated annealing algorithm towards optimisation of EDM process parameters

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]