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http://dx.doi.org/10.5762/KAIS.2020.21.1.560

Optimization of Process Parameters of Incremental Sheet Forming of Al3004 Sheet Using Genetic Algorithm-BP Neural Network  

Yang, Sen (Dept. of Mechanical Engineering, Kyungpook Nat'l Univ.)
Kim, Young-Suk (Dept. of Mechanical Engineering, Kyungpook Nat'l Univ.)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.1, 2020 , pp. 560-567 More about this Journal
Abstract
Incremental Sheet Forming (ISF) is a unique sheet-forming technique. The process is a die-less sheet metal manufacturing process for rapid prototyping and small batch production. In the forming process, the critical parameters affecting the formability of sheet materials are the tool diameter, step depth, feed rate, spindle speed, etc. This study examined the effects of these parameters on the formability in the forming of the varying wall angle conical frustum model for a pure Al3004 sheet with 1mm in thickness. Using Minitab software based on Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA), a second order mathematical prediction model was established to predict and optimize the wall angle. The results showed that the maximum forming angle was 87.071° and the best combination of these parameters to give the best performance of the experiment is as follows: tool diameter of 6mm, spindle speed of 180rpm, step depth of 0.4mm, and feed rate of 772mm/min.
Keywords
Incremental Sheet Forming; BPNN; Genetic Algorithm; Prediction; Optimization;
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Times Cited By KSCI : 5  (Citation Analysis)
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