TAILIEUCHUNG - Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm

(BQ) The present work is aimed at optimizing the surface roughness of die sinking electric discharge machining (EDM) by considering the simultaneous affect of various input parameters. | j o u r n a l o f m a t e r i a l s p r o c e s s i n g t e c h n o l o g y 2 0 9 ( 2 0 0 9 ) 1512–1520 journal homepage: Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm G. Krishna Mohana Rao a,∗ , G. Rangajanardhaa b , D. Hanumantha Rao c , M. Sreenivasa Rao a a b c JNTU College of Engineering, Hyderabad 85, AP, India Department of Mechanical Engineering, Hoseo University, South Korea Deccan College of Engineering and Technology, Hyderabad, AP, India a r t i c l e i n f o a b s t r a c t Article history: The present work is aimed at optimizing the surface roughness of die sinking electric dis- Received 27 August 2007 charge machining (EDM) by considering the simultaneous affect of various input parameters. Received in revised form The experiments are carried out on Ti6Al4V, HE15, 15CDV6 and M-250. Experiments were 28 March 2008 conducted by varying the peak current and voltage and the corresponding values of surface Accepted 2 April 2008 roughness (SR) were measured. Multiperceptron neural network models were developed using Neuro Solutions package. Genetic algorithm concept is used to optimize the weighting factors of the network. It is observed that the developed model is within the limits of the Keywords: agreeable error when experimental and network model results are compared. It is further EDM observed that the error when the network is optimized by genetic algorithm has come down Surface roughness to less than 2% from more than 5%. Sensitivity analysis is also done to find the relative influ- Hybrid model ence of factors on the performance measures. It is observed that type of material effectively Optimization influences the performance measures. Artificial neural network © 2008 Elsevier . All rights reserved. Genetic algorithm 1. Introduction The selection of appropriate machining .

• Cơ khí - Chế tạo máy
• Electro Discharge Machining
• Surface roughness
• Hybrid model Optimization
• Artificial neural network
• Genetic algorithm
• Experimental investigation of coated in electro discharge machining process
• Uncoated tools used in electro discharge machining process
• Machining of TI6AL4V
• The machining performance of Ti6Al4V
• Experimental investigation of various process parameters
• Various process parameters of powder mixed electro discharge machining process
• Machining of Al si alloy
• Material removal rate
• Tool wear rate
• Design of Experiments
• Response Surface Methodology
• Desirability Function Approach
• Process Modeling
• Multi Objective Optimization
• Grey Relational Analysis
• Regression Model
• Taguchi Method
• The EDM process
• Polynomial model
• Neuro fuzzy model
• Non conventional machining
• Process parameters
• Electro erosion
• Taguchi grey multi responses optimization
• Electrical discharge machining
• 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
• Electrical process parameters
• State of art in EDM process