Abstract
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.
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Li, D.Y., Peng, Y.H. & Yin, J.L. Optimization of metal-forming process via a hybrid intelligent optimization technique. Struct Multidisc Optim 34, 229–241 (2007). https://doi.org/10.1007/s00158-006-0075-1
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DOI: https://doi.org/10.1007/s00158-006-0075-1