SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

Chatzarakis, Ge; Di Trapani, F; Douvika, Mg; Foskolos, Fm; Fotos, A; Giovanis, Dg; Karypidis, Df; Livieratos, S; Roinos, K; Tsaris, Ak; Vaxevanidis, Nm; Vougioukas, E; Asteris, Pg

Risultato della ricerca: Paper

Abstract

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation 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 negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2016

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Electric discharge machining
Surface roughness
Neural networks
Surface discharges
Metal complexes
Backpropagation
Machining
Evaporation
Melting
Plasmas
Electrodes
Hot Temperature

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Chatzarakis, Ge; Di Trapani, F; Douvika, Mg; Foskolos, Fm; Fotos, A; Giovanis, Dg; Karypidis, Df; Livieratos, S; Roinos, K; Tsaris, Ak; Vaxevanidis, Nm; Vougioukas, E; Asteris, Pg (2016). SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS.

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS. / Chatzarakis, Ge; Di Trapani, F; Douvika, Mg; Foskolos, Fm; Fotos, A; Giovanis, Dg; Karypidis, Df; Livieratos, S; Roinos, K; Tsaris, Ak; Vaxevanidis, Nm; Vougioukas, E; Asteris, Pg.

2016.

Risultato della ricerca: Paper

Chatzarakis, Ge; Di Trapani, F; Douvika, Mg; Foskolos, Fm; Fotos, A; Giovanis, Dg; Karypidis, Df; Livieratos, S; Roinos, K; Tsaris, Ak; Vaxevanidis, Nm; Vougioukas, E; Asteris, Pg 2016, 'SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS'.
Chatzarakis, Ge; Di Trapani, F; Douvika, Mg; Foskolos, Fm; Fotos, A; Giovanis, Dg; Karypidis, Df; Livieratos, S; Roinos, K; Tsaris, Ak; Vaxevanidis, Nm; Vougioukas, E; Asteris, Pg. SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS. 2016.
Chatzarakis, Ge; Di Trapani, F; Douvika, Mg; Foskolos, Fm; Fotos, A; Giovanis, Dg; Karypidis, Df; Livieratos, S; Roinos, K; Tsaris, Ak; Vaxevanidis, Nm; Vougioukas, E; Asteris, Pg. / SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS.
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title = "SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS",
abstract = "Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation 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 negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.",
author = "{Chatzarakis, Ge; Di Trapani, F; Douvika, Mg; Foskolos, Fm; Fotos, A; Giovanis, Dg; Karypidis, Df; Livieratos, S; Roinos, K; Tsaris, Ak; Vaxevanidis, Nm; Vougioukas, E; Asteris, Pg} and Liborio Cavaleri",
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N2 - Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation 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 negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

AB - Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation 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 negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

UR - http://hdl.handle.net/10447/233010

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