Proposal of Multiple Laminate Configuration Candidates for Strength Test of Laminated Composites by Batch Bayesian Optimization

Abstract

The strength prediction of a fiber-reinforced laminated composite is a big issue to achieve an efficient research and development. This study aims to develop a prediction method of the strength variation of composite laminates as one of the material integration (MI) method by adopting the Bayesian optimization (BO) method. As the BO method, this study adopts a batch Bayesian optimization (BBO) to obtain multiple laminate configuration candidates that maximize the strength based on the results of the strength test with considering empirical constraints such as the contiguous number limit of the same orientation angles. In order to obtain the multiple laminate configurations, an improved genetic algorithm based on the distributed genetic algorithm with adaptive mutation and gene repair strategies is adopted. Through numerical examples based on the open hole tension (OHT) strength test data, efficiency of the proposed method is demonstrated.