Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Pattern Search Algorithms for Bound Constrained Minimization
SIAM Journal on Optimization
Moving least squares response surface approximation: Formulation and metal forming applications
Computers and Structures
Finite Elements in Analysis and Design
Shape optimization of the workpiece in the forging process using equivalent static loads
Finite Elements in Analysis and Design
| Hi-index | 0.00 |
Preform tool shape optimization using response surface method (RSM) was developed in this work. Neural network approximation model was employed for response surface construction in order to overcome the limitation of quadratic polynomial model in solving non-linear problems. A two-step axisymmetric forging problem was studied as an example using proposed method. Optimum was achieved by using pattern search optimization method to search response surface describing relationship between preform shape and die cavity fill ratio. In addition to that, with respect to the complexity of the optimum solution, the knowledge-based redesign concept was proposed. Simplified preform shape description model was built based on the knowledge extracted from previous optimization and additional shape optimization in terms of a new optimization objective was conducted to obtain a better redesign preform shape. Finally, comparison was made between the original optimal shape and redesigned one; better result was achieved by using the concept proposed.