Chiu Yi-Hung, Liao Ya-Hsuan, Juang Jia-Yang
Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan.
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Polymers (Basel). 2023 Jan 5;15(2):281. doi: 10.3390/polym15020281.
Designing composite materials with tailored stiffness and toughness is challenging due to the massive number of possible material and geometry combinations. Although various studies have applied machine learning techniques and optimization methods to tackle this problem, we still lack a complete understanding of the material effects at different positions and a systematic experimental procedure to validate the results. Here we study a two-dimensional (2D) binary composite system with an edge crack and grid-like structure using a Genetic Algorithm (GA) and Conditional Variational Autoencoder (CVAE), which can design a composite with desired stiffness and toughness. The fitness of each design is evaluated using the negative mean square error of their predicted stiffness and toughness and the target values. We use finite element simulations to generate a machine-learning dataset and perform tensile tests on 3D-printed specimens to validate our results. We show that adding soft material behind the crack tip, instead of ahead of the tip, tremendously increases the overall toughness of the composite. We also show that while GA generates composite designs with slightly better accuracy (both methods perform well, with errors below 20%), CVAE takes considerably less time (~1/7500) to generate designs. Our findings may provide insights into the effect of adding soft material at different locations of a composite system and may also provide guidelines for conducting experiments and Explainable Artificial Intelligence (XAI) to validate the results.
由于可能的材料和几何组合数量众多，设计具有定制刚度和韧性的复合材料具有挑战性。尽管各种研究已经应用机器学习技术和优化方法来解决这个问题，但我们仍然缺乏对不同位置材料效应的全面理解以及验证结果的系统实验程序。在这里，我们使用遗传算法（GA）和条件变分自动编码器（CVAE）研究了一种具有边缘裂纹和网格状结构的二维（2D）二元复合系统，该系统可以设计出具有所需刚度和韧性的复合材料。使用预测刚度和韧性与目标值的负均方误差来评估每个设计的适应度。我们使用有限元模拟来生成机器学习数据集，并对3D打印的试样进行拉伸试验以验证我们的结果。我们表明，在裂纹尖端后面而不是尖端前面添加软材料会极大地提高复合材料的整体韧性。我们还表明，虽然GA生成的复合材料设计精度略高（两种方法都表现良好，误差低于20%），但CVAE生成设计所需的时间要少得多（约为1/7500）。我们的发现可能为在复合系统的不同位置添加软材料的效果提供见解，也可能为进行实验和可解释人工智能（XAI）以验证结果提供指导。
