Automated regression workflow for interpretable deflection prediction in bio-inspired laminated composite plates

Abstract

This study introduces a powerful automated regression workflow (ARW) for accurately predicting deflection in bio-inspired laminated composite plates using diverse machine learning (ML) algorithms. The ARW significantly automates complex processes like hyperparameter optimization, model training, and performance evaluation, accelerating analytical insights. Six different ML regression models were systematically deployed, achieving an impressive average prediction accuracy, five models exceeding 99%, on a comprehensive finite element-generated data set. Notably, the eXtreme gradient boosting regression (XGBR) model exhibited superior performance (R² = 0.999, MAE = 0.010, RMSE = 0.013) on unseen data. Interpretability analyses using SHapley Additive exPlanations and local interpretable model-agnostic explanations on the optimal XGBR model consistently identified boundary conditions and the ratio of elastic moduli (E₁/E₂) as the most influential factors, followed by the aspect ratio (a/h) and loading type. This work establishes an efficient, accurate, and interpretable framework that accelerates the design and fundamental understanding of these complex composite structures, which can be further applied to numerous applications.

Graphical abstract

Keywords

ARW / interpretable machine learning / bio-inspired laminated composites / finite element analysis

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