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

• Main Authors: Kiyani, Elham, Sarvestani, Hamidreza Yazdani, Ravanbakhsh, Hossein, Behbahani, Razyeh, Ashrafi, Behnam, Rahmat, Meysam, Karttunen, Mikko
• Format: Journal Article
• Language: English
• Published: 19.05.2023
• Subjects: Computer Science - Computational Engineering, Finance, and Science
• DOI: 10.48550/arxiv.2305.11632

Topologically interlocking architectures can generate tough ceramics with attractive thermo-mechanical properties. This concept can make the material design pathway a challenging task, since modeling the whole design space is neither effective nor feasible. We propose an approach to design high-performance architectured ceramics using machine learning (ML) with data from finite element analysis (FEA). Convolutional neural networks (CNNs) and Multilayer Perceptrons (MLPs) are used as the deep learning approaches. A limited set of FEA simulation data containing a variety of architectural design parameters is used to train our neural networks, including learning how independent and dependent design parameters are related. A trained network is then used to predict the optimum structure from the configurations. A FEA simulation is run on the best predictions of both MLP and CNN algorithms to evaluate the performance of our networks. Although a limited amount of simulation data are available, our networks are effective in predicting the transient thermo-mechanical responses of possible panel designs. For example, the optimal design after using CNN prediction resulted in $\approx \! 30\%$ improvement in terms of edge temperature.