An Explainable Hybrid Finite Element-Machine Learning Framework for Performance Prediction and Optimization of Television Cushioning Packaging

Full abstract

Read the full abstract

The design of cushioning packaging for flat-screen television (TV) products relies heavily on repeated simulations, resulting in high development costs and low design efficiency. In this study, we propose a hybrid framework integrating finite element (FE) simulation, data augmentation and interpretable machine learning (ML) for rapid peak acceleration prediction and optimization of TV cushioning packaging. First, a total of 216 FE drop-impact simulation samples of TV cushioning packaging systems were generated using ANSYS Workbench, covering TV dimensions, liner type, liner density, liner thickness, drop height and peak acceleration. Mixup-based data augmentation and Bayesian optimization were then employed to develop and tune six ML models. All ML models trained on the original dataset achieved coefficients of determination (R2) ranging from 0.797 to 0.990. The Mixup-augmented XGBoost model achieved the best prediction performance, yielding R2 values of 0.998 and 0.983 for the training and testing datasets, respectively. SHAP analysis revealed that liner material type, liner density and liner thickness were the dominant factors affecting the protective performance of TV cushioning packaging. In addition, a web-based platform was developed based on the proposed FE–ML strategy to support the design exploration of feasible schemes for new TV products. The predictive capability of the proposed FE-ML framework was further evaluated using 22 independent cushioning packaging schemes, achieving an R2 of 0.926 and an average prediction error of 4.490 g. These results suggest that the proposed workflow can support the performance evaluation and optimization of TV cushioning packaging.