🤖 AI Summary
To address structural deformation in composite material curing arising from thermo-chemo-mechanical coupling, this paper proposes an efficient process optimization method integrating Gaussian process surrogate modeling with constrained Bayesian optimization (cBO). The objective is to minimize curing-induced deformation while strictly satisfying critical process constraints—particularly complete cure. This work represents the first systematic application of cBO to such multi-physics coupled optimization problems. Compared with conventional genetic algorithms requiring over 1,000 iterations, cBO achieves convergence in fewer than 50 iterations for both flat-plate and L-shaped composite structures, yielding over 20× computational speedup (>96% reduction in function evaluations) and maintaining a prediction error below 4%. The approach successfully attains dual optimization goals: strict constraint satisfaction and global minimization of residual deformation.
📝 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.