Experimental Investigation and Uncertainty Prediction of the Load-Carrying Capacity of Composite Double Hat for Lattice Core Sandwich Panels Using Artificial Neural Network

Abstract

Carbon fiber reinforced composites are promising candidates for building advanced multifunctional structures with superior properties that are suitable for the next generation of automotive and aircraft applications. This study presents an experimental investigation into the effect of the major orientation of the composite hat section on the crushing behavior and load-carrying capacity of a composite double hat structure. The variation in load carrying capacities due various measurement and manufacturing factors can significantly affect the design and thus safety. Therefore, the uncertainty in load carrying capacities is implicitly considered by identifying the maximum and minimum values of the load-carrying capacities at all displacement values. Artificial neural network-based models are then developed and compared using the Mean Squared Error (MSE) measure, with the objective to predict the load-carrying capacity range at each displacement value, which implicitly considers the uncertainty of results. Three samples of each arrangement are statistically analyzed and utilized in the training. The results show that the 'X' hat orientation outperforms the 'O' hat orientation in terms of load-carrying capacity. On the contrary, the 'O' hat orientation outperforms the 'X' hat orientation in terms of crash force efficiency with a total value of 0.6 for the former in comparison to 0.5 for the latter. A two layers ANN-models are found best in terms of performance with total RMSE values of 55.5 N for 'X' orientation and 515.8 N for 'O' orientation.