Advances in materials Research

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Modeling of surface roughness in electro-discharge machining using artificial neural networks

  • Cavaleri, Liborio (Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo) ;
  • Chatzarakis, George E. (Department of Electrical and Electronic Engineering Educators, School of Pedagogical and Technological Education) ;
  • Trapani, Fabio Di (Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo) ;
  • Douvika, Maria G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Roinos, Konstantinos (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Vaxevanidis, Nikolaos M. (Laboratory of Manufacturing Processes & Machine Tools, School of Pedagogical and Technological Education) ;
  • Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education)
  • Received : 2016.11.21
  • Accepted : 2017.07.11
  • Published : 2017.06.25
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Abstract

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

Keywords

References

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