Experimental investigation and multi-objective optimization of the effects of corner number, internal structure type and wall thickness parameters on the crashworthiness performance of 3D-printed structures

This paper presents a comprehensive experimental and numerical analysis of the crashworthiness characteristics of multi-cell thin-walled tubes fabricated from polylactic acid (PLA+) via Fused Deposition Modeling (FDM). The study evaluates the energy absorption performances and peak crushing forces of hexagonal, octagonal, and decagonal structures under quasi-static axial compression, specifically examining the influence of internal topology, number of edges, and wall thickness. Statistical analyses utilizing Taguchi orthogonal arrays identified wall thickness as the predominant factor governing mechanical behavior, while the subsequent optimization strategy yielded a 15.1% enhancement in energy absorption relative to the baseline model, attaining a value of 2173.32 J. A significant methodological novelty of this research lies in the implementation of one-hot encoding to quantify the categorical internal structure variable, thereby facilitating its integration with continuous design parameters. Leveraging this approach, a multi-objective optimization framework was established using Artificial Neural Networks (ANN) refined through grid search-based hyperparameter tuning and the NSGA-II algorithm. This integrated methodology successfully identified optimal design configurations that offer superior trade-offs between structural efficiency and safety compared to experimental observations.