Numerical simulation informed rapid cure process optimization of composite structures using constrained Bayesian optimization

Abstract

The present study aimed to solve the cure optimization problem of laminated composites through a statistical approach. The approach consisted of using constrained Bayesian Optimization (cBO) along with a Gaussian process model as a surrogate to rapidly solve the cure optimization problem. The approach was implemented to two case studies including the cure of a simpler flat rectangular laminate and a more complex L-shaped laminate. The cure optimization problem with the objective to minimize cure induced distortion was defined for both case studies. The former case study was two-variable that is used two cure cycle parameters as design variables and was constrained to achieve full cure, while the latter was four-variable and had to satisfy constraints of full cure as well as other cure cycle parameters. The performance of cBO for both case studies was compared to the traditional optimization approach based on Genetic Algorithm (GA). The comparison of results from GA and cBO including deformation and final degree of cure showed significant agreement (error <4%). The computational efficiency of cBO was calculated by comparing the convergence steps for GA (>1000) and cBO (<50). The computational efficiency of cBO for all optimization cases was found to be >96%. The case studies conclude that cBO is promising in terms of computational time and accuracy for solving the cure optimization problem.