A data-driven scheme to search for alternative composite materials

Abstract

Here, we propose an interaction analysis method, which quantitatively evaluates the interactions among constituent materials in composites that consist of matrix (e.g. resin), fillers, and additives (e.g. fiber-reinforced plastics). Using this method, we developed a data-driven scheme that is capable of identifying alternative constituent materials. The scheme enabled the search for new composites with the same performance as the original composite. The proposed scheme was also found useful for composites with complex interactions, where the features of constituent materials (e.g. the addition ratio) exhibited linear and exponential relationships with the performance. To demonstrate the utility of the proposed scheme, we searched for alternative constituent materials, which are involved in the interaction with the largest contribution to the performance (i.e. bending modulus). The bending moduli of the resulting composites were comparable to the original composite, thereby verifying the utility of the proposed scheme.

Keywords

• composite
• interaction
• unsupervised learning
• alternative material search