Optimization of metal-forming process via a hybrid intelligent optimization technique
In recent years, finite element simulation has been increasingly combined with optimization techniques and applied to optimization of various metal-forming processes. The robustness and efficiency of process optimization are critical factors to obtain ideal results, especially for those complicated metal-forming processes. Gradient-based optimization algorithms are subject to mathematical restrictions of discontinuous searching space, while nongradient optimization algorithms often lead to excessive computation time. This paper presents a novel intelligent optimization approach that integrates machine learning and optimization techniques. An intelligent gradient-based optimization scheme and an intelligent response surface methodology are proposed, respectively. By machine learning based on the rough set algorithm, initial total design space can be reduced to self-continuous hypercubes as effective searching spaces. Then optimization algorithms can be implemented more effectively to find optimal design results. An extrusion forging process and a U channel roll forming process are studied as application samples and the effectiveness of the proposed approach is verified.
finite element simulation response surface methodology rough set algorithm metal forming sequential quadratic programming