Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

  • Muhammad Akmal Mohd Zakaria Advanced Materials and Precision Engineering Advanced Manufacturing Centre (AMC), Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia. http://orcid.org/0000-0001-6960-1902
  • Raja Izamshah Raja Abdullah Advanced Materials and Precision Engineering Advanced Manufacturing Centre (AMC), Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia. http://orcid.org/0000-0002-1985-6736
  • Mohd Shahir Kasim Advanced Materials and Precision Engineering Advanced Manufacturing Centre (AMC), Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia. http://orcid.org/0000-0002-8483-8227
  • Mohamad Halim Ibrahim Mechanical Engineering Department, Politeknik Merlimau Melaka, Jalan Jasin,77300 Merlimau, Melaka, Malaysia.
Keywords: WEDM, ANN, Sustainability, Productivity, Water-based dielectric

Abstract

Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process.

Author Biographies

Raja Izamshah Raja Abdullah, Advanced Materials and Precision Engineering Advanced Manufacturing Centre (AMC), Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
Associate Professor Dr.
Mohd Shahir Kasim, Advanced Materials and Precision Engineering Advanced Manufacturing Centre (AMC), Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
Associate Professor Dr.

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Published
2019-06-15
Section
Articles