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Artificial Intelligence Review

, Volume 52, Issue 1, pp 671–706 | Cite as

Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys

  • Yusliza YusoffEmail author
  • Azlan Mohd Zain
  • Astuty Amrin
  • Safian Sharif
  • Habibollah Haron
  • Roselina Sallehuddin
Article

Abstract

As surveyed, many efforts have been made to model the performances of electrical discharge machining (EDM) using artificial neural network (ANN). However, the selections of the network parameters were mostly prepared in a random manner, resulting to unnecessary trials. Thus, orthogonal array (Taguchi) is employed in the procedure of network function and network architecture assortment to avoid excessive random trial experimentations. This proposed orthogonal based ANN modelling is employed on WEDM of Ti–48Al intermetallic alloys. Meanwhile modified multi objective genetic algorithm (multiGA) is used as the optimization technique. Material removal rate (MRR), surface roughness (Ra), cutting speed (Vc) and width of kerf (Dk) are the machining performances considered in this study. Five machining parameters observed from the previous researches are chosen as significant factors to the machining performances in this study, which are pulse on time, pulse off time, peak current, feed rate and servo voltage. Experimental studies are carried out to verify the machining performances suggested by this approach. Feed forward back propagation neural network (FFNN) is found to be the best network type on the selected dataset. Two hidden layer 5–6–6–4 FFNN showed the most precise and generalized network architecture with very good prediction accuracy. The proposed approach, OrthoANN, reduced ANN experimentation time by a large scale and produced viable results for machining optimization when integrated with multiGA.

Keywords

Orthogonal array Regression Artificial neural network (ANN) Multi objective genetic algorithm Optimization Electrical discharge machining (EDM) 

Notes

Acknowledgements

Special appreciations to editor and all reviewers on the useful advices and comments provided. The authors greatly acknowledge the Research Management Centre, Universiti Teknologi Malaysia (UTM), Ministry of Higher Education Malaysia (MOHE) (GUP—vot. No. 16H81, FRGS—vot. No. 4F378) and international grant (ERL—vot. No. 4B310) for financial support.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Computer Science, Faculty of ComputingUTM SkudaiSkudaiMalaysia
  2. 2.UTM Razak School of Engineering and Advanced TechnologyUTM Kuala LumpurKuala LumpurMalaysia
  3. 3.Department of Manufacturing and Industrial Engineering, Faculty of Mechanical EngineeringUTM SkudaiSkudaiMalaysia

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