A Multimodal Machine Learning Approach for Identification of Mechanical Properties in  <scp>CFRP</scp> Composites

ABSTRACT Accurate and efficient identification of the mechanical properties of carbon fiber reinforced polymer (CFRP) composites is crucial for their design and application. This paper proposes a machine learning (ML) approach based on a multimodal neural network to identify orthotropic mechanical properties including elastic modulus, Poisson's ratio, shear modulus, as well as tensile and in‐plane shear strengths. The architecture concurrently processes distinct data modalities: a multi‐layer perceptron analyzes load–displacement curves, while a convolutional neural network extracts spatial features from strain and displacement fields. The validity and reliability of the identified parameters are confirmed through a three‐point bending simulation, and an ablation study is performed to demonstrate the superiority of the proposed architecture. The results indicate that the proposed method achieves a mean relative error of 5.7% compared to experimental results, which outperforms a finite element (FE)‐based inverse method. In terms of efficiency, the model's inference time of only 0.21 s is approximately 5600 times faster than a single FE analysis. The three‐point bending simulation shows high consistency with experimental results. Finally, an ablation study confirms the superiority of the multimodal neural network. The full model achieves a coefficient of determination of 0.92, outperforming the two single‐modality models by 21% and 16%, respectively.