TAILIEUCHUNG - Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II

(BQ) Present study attempts to model and optimize the complex electrical discharge machining (EDM) process using soft computing techniques. Artificial neural network (ANN) with back propagation algorithm is used to model the process. As the output parameters are conflicting in nature so there is no single combination of cutting parameters, which provides the best machining performance. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Experiments have been carried out over a wide range of machining conditions for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work. | Journal of Materials Processing Technology 186 (2007) 154–162 Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II Debabrata Mandal, Surjya K. Pal ∗ , Partha Saha Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India Received 21 June 2006; received in revised form 15 September 2006; accepted 14 December 2006 Abstract Present study attempts to model and optimize the complex electrical discharge machining (EDM) process using soft computing techniques. Artificial neural network (ANN) with back propagation algorithm is used to model the process. As the output parameters are conflicting in nature so there is no single combination of cutting parameters, which provides the best machining performance. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Experiments have been carried out over a wide range of machining conditions for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work. © 2006 Elsevier . All rights reserved. Keywords: Electrical discharge machining (EDM); Artificial neural network (ANN); Multi-objective optimization; Genetic algorithm (GA) 1. Introduction Electrical discharge machining (EDM) has become one of the most extensively used non-conventional material removal process. Its unique feature of using thermal energy to machine electrically conductive parts regardless of hardness has been its distinctive advantage in the manufacture of mould, die, automotive, aerospace and surgical component [1]. Optimal selection of process parameters is very much essential as this is a costly process to increase production rate considerably by reducing the machining time. Material removal rate (MRR) and tool

• Cơ khí - Chế tạo máy
• Electrical discharge machining
• The EDM process
• Artificial neural network
• Multi objective optimization
• Genetic algorithm
• Understanding electrical discharge machining
• Various Electric Discharge Machines
• Keeping Abreast With EDM Technology
• The Machining Revolution
• A strip electrode
• The machining principle
• Surface modification
• Powder metallurgy electrodes
• Surface finish
• Electrode wear
• Inconel 718
• Response Surface Methodology
• Multi Objective Particle SwarmOptimization
• The electrical discharge machining
• The EDM processes
• Process parameters
• Stainless steel
• Optimization of machining parameters
• Fuzzy logic analysis
• Analysis of variance
• Machining precision and accuracy
• Green manufacturing
• Multi attribute decision making
• Fuzzy TOPSIS methods
• Silicon carbide
• Die sinking EDM
• Ceramic composite
• Orthogonal array
• Statistical analyses
• Decision making model for green EDM
• Electro Discharge Machining
• Material Removal Rate
• Polynomial model
• Neuro fuzzy model
• Non conventional machining
• Journal of Science and Technology
• Analysis of surface layers of hot
• Analysis of surface layers of hot – forging die
• Hot forging die following die sinking
• Taguchi methods
• Ideal function
• Energy distribution
• Sinking electro discharge machining
• Mathematical model
• Ultrasonic vibration
• Dry EDM
• Powder additives
• EDM in water
• Multi response optimization
• Powder mixed electrical discharge machining
• Surface micro hardness
• Pulse on time
• Pulse off time
• Tool steel
• Surface roughness
• Examination of machining parameters
• EDM of tool steel
• Metal matrix composite
• Temperature stability
• Duty factor
• Titanium alloys
• Electrical process parameters
• State of art in EDM process
• Process parameters of wire cut
• 90CrSi tool steel
• Wire cut EDM
• Tool steel machining
• AISI P20 tool steel
• Fuzzy TOPSIS
• Sensitivity analysis
• The effect of multiple discharges
• Thermal model
• Roughness prediction
• Predictive model
• Discrimination of discharging pulses
• System identification