Artificial neural network prediction of wire electrical discharge machining properties on sintered porous NiTi shape memory alloy. (2018)

• Record Type: Journal Article
• Authors: Velmurugan, C.; Senthilkumar, V.; Dinesh, S.; Arul kirubakaran, D.
• Is Part Of: Materials today. Volume 5:Number 2(2018)Part 2
• Page Start: 8382
• Page End: 8390
• Publication Date: 2018
• Subjects: Porous -- NiTi-SMA -- Artificial neural network -- Genetic algorithm -- WEDM; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2017.11.532
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: This article presents the characterisation of porous nickel titanium shape memory alloy during wire electrical discharge machining process with different input process parameters such as servo voltage, pulse on time, pulse off time, current and wire speed. The performance of WEDM process on porous NiTi alloy prepared through powder metallurgy route was measured as the output characteristics such as material removal rate (MRR) and surface roughness (Ra). Taguchi design with L18 orthogonal array was used for depicting the WEDM experimental parameters with 5 factors and 2-3 mixed level for optimization. Artificial neural network model was developed to predict the performance of WEDM process under two methods of connections such as multilayer normal feed and full feed forward. ANN model was predicted using three learning algorithms such as Incremental Back Propagation (IBP), Batch Back Propagation (BBP) and Quick prop (QP) with variations of neurons from 5 to 20 in the hidden layer. Genetic algorithm was employed for optimization the WEDM process parameters and output variables those were closely matched with experimental values. Based on the results, servo voltage is the most influencing factor on MRR and Ra. The best algorithms were selected as IBP and BBP with 20 neurons for multilayer normal and full feed forward method respectively that values given as an input fitting function for GA. The confirmation test was executed using GA and those values were coincided with the experimental values. The best WEDM process parameters were optimized using full feed forward method to provide better MRR and product finish.