Modelling of the Parameters of EDM in Gas Based on Back Propagation Neural Network

Yan Cherng Lin; Han Ming Chow; Hsin Min Lee; Jia Feng Liu

doi:10.4028/www.scientific.net/MSF.926.11

Paper Titles

Preface

Effect of Final Rapid Cooling Temperature on Ultra-Fine Carbides of Ti-Mo Ferrite Matrix Microalloyed Steel
p.3

Modelling of the Parameters of EDM in Gas Based on Back Propagation Neural Network
p.11

Mechanical Interlocking of an Aluminum Alloy and SS400 Structural Steel through Friction-Stir Spot Forming (FSSF)
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A Study on Galvanic Current Variation of Zn Sacrificial Anode Made by Including of Additive in Solutions with Various Conductivities
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Effect of Osmotic Pressure by Salt Concentration on Corrosion Resistance of Anti-Corrosive Paint
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Synthesis and Properties of Phenyl Silicone Resin Reinforced Addition Type Liquid Phenyl Silicone Rubber
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Thermal Stability and Kinetics of the Thermal Degradation of Thermal Conductive Silicone Rubber Filled with Al₂O₃ and ZnO
p.45

Investigation of the Influence of Heat Treatment on the Strength of Sandwich Structures
p.51

HomeMaterials Science ForumMaterials Science Forum Vol. 926Modelling of the Parameters of EDM in Gas Based on...

Modelling of the Parameters of EDM in Gas Based on Back Propagation Neural Network

Abstract:

The aim of this study is to develop a predicted model of the machining parameters with relation to material removal rate (MRR) and surface roughness (SR) of electrical discharge machining (EDM) in gas. The experimental tasks were implemented by a specific design of experimental method named central composite design (CCD) method. The mathematical prediction models between operating parameters and machining characteristics based on artificial neural network (ANN) were established. The back propagation neural network (BPNN) was employed to construct the architecture of the input layer, the hidden layer and the output layer to build the ANN model. Moreover, the weight and the bias values were examined by the steepest descent method (SDM) with the training data. Thus, the suitable ANN models were established with the acquired weight and bias values. The essential parameters of the EDM in gas such as peak current (I_p), pulse duration (t_p), gas pressure (G_P), servo reference voltage (S_v) were chosen to investigate the effects on MRR and SR. The developed ANN model with 4 input variables on the input layer, one hidden layer with 5 neurons, and 2 response variables on the output layer was obtained by the training with 30 experimental data. Moreover, as the prediction values obtained from the ANN compared with the 5 testing data, the error falls in the rage of 5% indicating the developed ANN is appropriate and predictable. Moreover, the developed ANN model can be used to predict the machining characteristics such as MRR and SR for the EDM in gas with various parameter settings.

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Materials Science Forum (Volume 926)

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11-16

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https://doi.org/10.4028/www.scientific.net/MSF.926.11

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July 2018

Authors:

Yan Cherng Lin*, Han Ming Chow, Hsin Min Lee, Jia Feng Liu

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Artificial Neural Network, EDM in Gas, Material Removal Rate, Surface Roughness

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