Microstructural design of composite materials using fixed-grid modeling and noise-resistant smoothed Kriging-based approximate optimization

This paper discusses a microstructural optimization of composites using a fixed-grid modeling technique and an approximate optimization approach. In particular, we design a microscopic structure of composites to improve its reliability. As the response surface becomes nonlinear and inaccuracies may be included in the sampling results in using the fixed-grid model, applicability of several approximation methods such as a polynomial-based approach, neural network, and Kriging method are investigated. Especially, the inaccuracy is regarded as a noise in sampling data, and applicability of the noise-resistant smoothed Kriging (ns-Kriging) is investigated. As an example, cross-sectional shape of fiber in a unidirectional fiber-reinforced plastics is optimized. By applying several approximate optimization methods to the problem, applicability of those methods is investigated. Next, cross-sectional shape of fibers in a composite plate subject to bending and compression is optimized using the ns-Kriging-based method. Numerical results illustrate applicability of the proposed approach.