Designing high-performance composites requires integrating tasks, including material selection, structural arrangement, and mechanical property characterization. Accurate prediction of composite mechanical properties requires a comprehensive understanding of their mechanical response, particularly the failure mechanisms under high deformations. As traditional computational methods struggle to exhaustively explore every composite configuration in the vast design space for optimal design search, machine learning offers rapid identification of optimal composite designs. This study presents a cGAN-based deep learning model for predicting stress- strain curves directly from composite structures using an image-to-vector approach. The model incorporates fully connected layers within a U-Net generator for stress-strain curve generation and utilizes a PatchGAN discriminator for realism assessment. This end-to-end mapping from structures to mechanical response effectively eliminates the need for extensive simulations and labor-intensive post-analyses. Phase-field simulations were conducted to model the material failure process, generating stress-strain curves for various composite structures used as ground truth data to train and test the surrogate model. This study incorporates various composite structures in the dataset, including random (RS), layered (LS), chessboard-like (CS), soft-scaffold (SS), and hard-scaffold (HS), enhancing the representation of design diversity. Despite being trained on a limited dataset (approximately 1.5% for each bio-mimetic structure and 10 -72 % for RS composites), the model achieves highly accurate predictions in stress-strain curves, with MAE loss converging to 0.01 for training and 0.05 for testing after 2 million iterations. High evaluation scores on training data (R-2 > 0.997, . 997, MAPE < 1.08%) . 08% ) and testing data (R-2 > 0.946, . 946, MAPE < 5.53%) . 53% ) demonstrate the model's accuracy in predicting mechanical properties such as Young's modulus, strength, and toughness across all composite structures. Overall, the study provides a proof of concept for using machine learning to simplify the design process, demonstrating its potential for solving inverse composite design problems.
