Journal
MATERIALS AND MANUFACTURING PROCESSES
Volume 28, Issue 10, Pages 1124-1132Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10426914.2013.773024
Keywords
BPNN; GA; Modeling; Optimization; RSM; WEDM
Funding
- National Natural Science Foundation of China (NSFC) [51175207, 51121002]
- National Key Technology RD Program [2012BAF13B07]
- Science and Technology Planning Project of Guangdong Province [2012B011300015]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available