چکیده مقاله
In the present study, a unified coupled artificial neural network genetic algorithm ANNGA based methodology is proposed for the modeling and optimal selection of process parameters involved in electrical discharge machining EDM process Firstly, based onthe prior knowledge of the physics of process mechanism as well as the EDM machine characteristics, the controllable input variables were identified as the discharge current I ,pulse on time Ton and pulse off time Toff Having conducted a set of preliminary and screening experiments to specify the stable working domains of these inputs, a 43 full factorial design scheme was employed to plan the training experiments of the backpropagation neural network model The experiments were done in the planing mode with both flat tool and work piece electrodes having equal diameters Heat treated CK45 carbon steel up to 60 RC and commercial cupper were used as work piece and tool materials, respectively Material removal rate MRR and surface roughness Ra were chosen as the response outputs There upon, a 3 8 6 2 size feed forward back propagation neural network was developed to establish the process model Testing the generalization capabilities of the model by a set of new data not being used in the training phase, the second stage begins by combining the genetic algorithm technique with the neural model to find optimum machining conditions leading to the highest possible MRR The multi objective optimization problem was categorized as finishing Ra ≤ 4μm , semifinishing Ra ≤ 8μm , and roughing Ra ≤ 12μm regimes, from which a group of optimal input parameters were obtained in each case, satisfying the Ra constraint and yielding the maximum MRR, simultaneously Finally, the modeling and traded off selected optimal results were also interpreted and verified experimentally to validate the adopted approach The observed errors are all in acceptable ranges less than 10% which show a good agreement with simulation results and confirm the feasibility and effectiveness of the implemented strategy
کلیدواژهها
نویسندگان
S Assarzadeh
Ph D Student, Specific Machinings Laboratory S M L , Department of Mechanical Engineering, K N Toosi University of Technology, P O Box: ۱۹۳۹۵ ۱۹۹۹, Tehran, Iran,
M Shamsi
B Sc Graduate, Mashhad Technical College, P O Box: ۹۱۷۳۵ ۱۷۱, Mashhad, Iran
M Ghoreishi
Associate Professor, Department of Mechanical Engineering, K N Toosi University of Technology,P O Box: ۱۹۳۹۵ ۱۹۹۹, Tehran, Iran
شیوه ارجاع
Assarzadeh, S and Shamsi, M and Ghoreishi, M,1388,A Coupled Artificial Neural Network-Genetic Algorithm (ANN-GA) Based Approach to Process Modeling and Optimization of Electrical Discharge Machining (EDM) Parameters,10th Iranian Conference on Manufacturing Engineering (ICME 2010),Babol
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