Optimization of Thermoplastic Elastomer (TPE) Components for Aerospace Structures Using Computerized Data-Driven Design

A data-driven optimization framework that integrates machine learning surrogate models, finite element analysis (FEA), and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer (TPE) parts for aerospace applications. By using FEA simulations and experiments, a database of input design parameters (e.g., geometry and structural shape modifier) is generated. Afterwards, we train surrogate models (e.g., Gaussian Process Regression, neural networks) to approximate mappings from design space to performance space. Finally, we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop (such as NSGA-II or gradient-based methods). The novelty of this approach is demonstrated by employing highly simplified surrogate models, including an artificial neural network (ANN) with 10 hidden neurons trained on analytically generated synthetic data. The proposed methodology has been validated using an aerospace-related case study: a vibration-damping plate. Compared with the baseline configuration, Pareto-optimal designs identified by the proposed framework achieved a reduction in maximum deflection of 23%–28% and a reduction in von Mises stress of 18%–24%, depending on the selected trade-off solution, as the number of full FEA simulations required for optimization was reduced from 500 to 50. This framework enables faster design of TPE components for aerospace systems. Validation against high-fidelity ANSYS simulations showed a mean error of ~1.18% and a maximum deviation of ~2.6%.