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RSM and BPNN Modeling in Incremental Sheet Forming Process for AA5052 Sheet: Multi-Objective Optimization Using Genetic Algorithm
| Content Provider | MDPI |
|---|---|
| Author | Xiao, Xiao Kim, Jin-Jae Hong, Myoung-Pyo Yang, Sen Kim, Young-Suk |
| Copyright Year | 2020 |
| Description | In this study, the response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the forming parameters of AA5052 in incremental sheet forming (ISF). The optimization objectives were maximum forming angle and minimum thickness reduction whose values vary in response to changes in production process parameters, such as the tool diameter, step depth, tool feed rate, and tool spindle speed. A Box–Behnken experimental design was used to develop an RSM and BPNN model for modeling the variations in the forming angle and thickness reduction in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process using the GA. The results showed that RSM effectively modeled the forming angle and thickness reduction. Furthermore, the correlation coefficients of the experimental responses and BPNN predictions of the experiment results were good with the minimum value being 0.97936. The Pareto optimal solutions for maximum forming angle and minimum thickness reduction were obtained and reported. The optimized Pareto front produced by the GA can be a rational design guide for practical applications of AA5052 in the ISF process. |
| Starting Page | 1003 |
| e-ISSN | 20754701 |
| DOI | 10.3390/met10081003 |
| Journal | Metals |
| Issue Number | 8 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-07-25 |
| Access Restriction | Open |
| Subject Keyword | Metals Manufacturing Engineering Incremental Sheet Forming Rsm Bp Neural Network Genetic Algorithm Multi-objective Optimization |
| Content Type | Text |
| Resource Type | Article |
