Comparative Analysis of Deep Learning Approaches for Predicting Thermomechanical Behavior of Shape Memory Polymers

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Thermomechanical constitutive modeling is crucial for understanding and designing shape memory polymers (SMPs) for advanced engineering applications. Traditional approaches are often time-consuming and computationally expensive which requires the development of more efficient and accurate methods. In this paper, we propose a Transformer-based deep learning model to predict the thermomechanical behavior of semicrys-talline two-way shape memory polymers (2W-SMPs) under thermomechanical cycles. By leveraging its ability to capture complex, time-dependent patterns, the framework accurately models the intricate relationships between polymer strain, time, temperature, and stress. A comparative analysis with other deep learning models demonstrates that the Transformer excels in accuracy and robustness by capturing intricate dependencies and non-linearities in the data. The test results demonstrate that our proposed Transformer model achieved a root mean squared error (RMSE) of 0.3365, outperforms the traditional deep learning models such as the feedforward neural network (FNN), convolutional neural network (CNN), and long short-term memory (LSTM) models which achieved RMSE values of 6.78, 17.35, and 13.85, respectively. This study highlights the potential of the Transformer model as a powerful tool for predicting material behavior and reducing the time and resources required for constitutive modeling.

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