The technological indexes are closely related to processing parameters in electrical discharge machining (EDM). In general, the output objectives only can be predicted with the help of the past processing laws and empirical methods before actually executing. In view of this situation, a prediction model is presented and established on the base of mathematical method of support vector regression to determine the technological indexes in EDM. The genetic algorithm (GA) is adopted as an optimization solver to seek the best variables in this model. In the case of processing 8418 steel, the proper process parameters are selected by orthogonal experimental method and empirical method, and the results are recorded. To ensure accuracy of prediction models of technological indexes in EDM, the experimental data are divided into a training set and a test set. The prediction model is trained through the training data set. The results show that the model of machining time has the mean squared error TMSE of 0.95×10-4 and the correlation coefficient TR2of 0.999 1; the model of material removal rate has the mean squared error MRRMSEof 1.02×10-4 and the correlation coefficient MRRR2 of 0.999 3; the model of electrode wear rate has the mean squared error EWRMSE of 1.11×10-4 and the correlation coefficient EWR R2of 0.998 9. Then those models are proved by the test samples, and the relative error is less than 5%. Thus the accuracy and effectiveness for the present models are verified.
YU Jianwu
,
HE Lihua
,
DUAN Wen
,
SHEN Xiang
,
YI Cheng
. Prediction Model of Technological Indexes in EDM of 8418 Steel[J]. Journal of Mechanical Engineering, 2016
, 52(11)
: 207
-212
.
DOI: 10.3901/JME.2016.11.207
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