دوره 16، شماره 1 - ( 12-1404 )
جلد 16 شماره 1 صفحات 4999-4980 |
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Nejati Ajbisheh I, Rezapour J, Gohari Rad S. Experimental Data-Based Machine Learning Prediction of Crashworthiness Parameters and Energy Absorption. ASE 2026; 16 (1) :4980-4999
URL: http://ase.iust.ac.ir/article-1-745-fa.html
URL: http://ase.iust.ac.ir/article-1-745-fa.html
Experimental Data-Based Machine Learning Prediction of Crashworthiness Parameters and Energy Absorption. Automotive Science and Engineering. 1404; 16 (1) :4980-4999
چکیده: (305 مشاهده)
Thin-walled tubes are effective crashworthiness structures that absorb energy under axial loading, while preventing the transmission of injurious acceleration and excessive forces to the protected section and thereby reducing damage severity. This study presents a high-accuracy artificial neural network (ANN) framework for predicting the dynamic response and impact resistance of thin-walled steel tubes under high-velocity axial impacts. The model was developed using a hybrid dataset comprising 300 experimental impact tests and 4000 finite element (FE) simulations, with systematic variations in tube geometry and impact conditions. After parameter optimization, the final model consisted of a 24-layer network with 180 neurons per layer and achieved high accuracy (R-value above 0.985). Error assessments across the four physical criteria (PFE, MFE, AEE, and SHE) show that the ANN predicts peak force, mean force, absorbed energy, and shortening with average errors generally below 10%, demonstrating strong predictive capability. Overall, the model not only offers a much faster alternative to FE simulations but also accurately reproduces oscillatory behavior and deformation progression, making it a reliable tool for impact response prediction.
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