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

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.

Towards Sustainable Production Processes Reengineering: Case Study at INCOM Egypt

“…The work by [47] uses ANN technology to develop a predictive model of sustainability measures that improve the quantity and quality of production. They developed a model based on artificial intelligence that delivers an accurate and efficient forecast, indicating that the constructed model has a high level of accuracy in predicting sustainable measures.…”

Analysis of control factors and surface integrity during wire-EDM of Inconel 718 alloy using T-GRA approach

“…Some findings concerning the effect of machining methods on surface morphology and stereo-metric surface parameters in wire-EDM of hard machined materials which also include Inconel alloys, are discussed [16]. Simultaneously, several studies can be reported in literature that discusses the challenges correlated with precision machining of different materials with wire-EDM [17]. The findings of magnesium alloy in view of efficient machining operation with different facets of the performance and reliability of the magnesium alloy are discussed using wire-EDM [18], [19].…”