Prediction of Impact Information of Composites Laminates Considering the Practicality

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Published Sep 4, 2023
Saki Hasebe Ryo Higuchi Tomohiro Yokozeki Shin-ichi Takeda

Abstract

Recently, carbon fiber reinforced plastics (CFRP) have been used in various applications, including aircraft. Because they are vulnerable to out-of-plane loads, internal and external
damage occurs when foreign objects impact them. Internal damage that can affect residual properties is difficult to find and judge from the outside without special devices, which are
highly costed and are sometimes difficult to conduct in some locations. In this study, surface contour information was obtained from impact tests on CFRP laminates, and the
predictability of compression after impact (CAI)strength was investigated using a conventional single-task random forest model, and a decision tree-based multi-task learning model with other objective variables related to impact tests. The models estimated CAI strength with around 75% R2, and the conventional single-task learning model showed the highest value. The importance of each model indicated that factors that contribute to impact-related objective variables (impactor shape, delamination area, and delamination length) and those to CAI strength do not have a strong relationship.

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Keywords

Machine learning, Composites, Impact

References
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Section
Regular Session Papers