期刊
MATERIALS AND MANUFACTURING PROCESSES
卷 25, 期 6, 页码 467-475出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10426910903365760
关键词
Artificial neural network (ANN); Genetic algorithms; Micro-electric discharge machining (mu-EDM); Optimization
The present work reports on the development of modeling and optimization for micro-electric discharge machining (-EDM) process. Artificial neural network (ANN) is used for analyzing the material removal of mu-EDM to establish the parameter optimization model. A feed forward neural network with back propagation algorithm is trained to optimize the number of neurons and number of hidden layers to predict a better material removal rate. A neural network model is developed using MATLAB programming, and the trained neural network is simulated. When experimental and network model results are compared for the performance considered, it is observed that the developed model is within the limits of the agreeable error. Then, genetic algorithms (GAs) have been employed to determine optimum process parameters for any desired output value of machining characteristics. This well-trained neural network model is shown to be effective in estimating the MRR and is improved using optimized machining parameters.
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