Designing architectured ceramics for transient thermal applications using finite element and deep learning

Topologically interlocking architectures have demonstrated the potential to create durable ceramics with desirable thermo-mechanical properties. However, designing such materials poses challenges due to the intricate design space, rendering traditional modeling approaches ineffective and impractical. This paper presents a novel approach to designing high-performance architectured ceramics by integrating machine learning (ML) techniques and finite element analysis (FEA) data. The design space of interlocked architectured ceramics encompasses tiles with varying angles and sizes. The study considers three configurations 3x3 , 5x5 , and 7x7 arrays of tiles with five sets of interlocking angles (5 circle,10 circle,15 circle,20 circle,and25 circle) . By training ML models, specifically convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) using FEA simulation data, we establish correlations between architectural parameters and thermo-mechanical characteristics. A grid comprising all possible designs was generated to predict high-performance architectured ceramics. This grid was then fed into the networks that were trained using results from the FEA simulation. The predicted results for all possible interpolated designs are utilized to determine the optimal structure among the configurations. The goal is to identify the optimal interlocked ceramics that minimize the out-of-plane deformation for thermal shielding and maximize heat absorption for heat sink applications. To validate the performance of the outcomes, FEA simulations were conducted on the best predictions obtained from both the MLP and CNN algorithms. Despite the limited amount of available simulation data, our networks demonstrate effectiveness in predicting the transient thermo-mechanical responses of potential panel designs. Notably, the optimal design predicted by CNN led to approximate to 30% improvement in edge temperature.