Surrogate-assisted optimization frameworks are of great use in solving practical computationally expensive process-design-optimization problems. In this chapter, a framework for design optimization is introduced that makes use of neural-network-based surrogates in lieu of actual analysis to arrive at optimum process parameters. Theperformance ofthe algorithm is studied using a numberofmathematical benchmarks to instill confidence on its performance before reporting the results of a springback minimization problem. The results clearly indicate that the framework is able to report optimum designs with a substantially low computational cost while maintaining an acceptable level of accuracy.

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