期刊
MATERIALS AND MANUFACTURING PROCESSES
卷 28, 期 10, 页码 1124-1132出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10426914.2013.773024
关键词
BPNN; GA; Modeling; Optimization; RSM; WEDM
资金
- National Natural Science Foundation of China (NSFC) [51175207, 51121002]
- National Key Technology RD Program [2012BAF13B07]
- Science and Technology Planning Project of Guangdong Province [2012B011300015]
This study analyzed the workpiece surface quality (Ra) and the material removal rate (MRR) on process parameters during machining SKD11 by medium-speed wire electrical discharge machining (MS-WEDM). An experimental plan for composite design (CCD) has been conducted according to methods response surface methodology (RSM) and subsequently to seek the optimal parameters. The experimental data were utilized to model MRR and Ra under optimal parameter condition by a backpropagation neural network combined with genetic algorithm (BPNN-GA) method. Eventually, the comparisons between the results from BPNN-GA and those from the RSM demonstrate that BPNN-GA method is a more effective way for optimizing MS-WEDM process parameters.
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