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A Machine Learning Technique to Predict Biaxial Failure Envelope of Unidirectional Composite Lamina

FAISAL BHUIYAN, LARS KOTTHOFF, RAY FERTIG, III

Abstract


A machine learning technique was used to predict static, failure envelopes of unidirectional composite laminas under combined normal (longitudinal or transverse) and shear loading at different biaxial ratios. An artificial neural network was chosen for this purpose due to their superior computational efficiency and ability to handle nonlinear relationships between inputs and outputs. Training and test data for the neural network were taken from the experimental composite failure data for glass- and carbonfiber reinforced epoxies provided by the world-wide failure exercise (WWFE) program. A quadratic, stress interactive Tsai-Wu failure theory was calibrated based on the reported strength values, as well as optimized from the experimental failure data points. The prediction made by the neural network was compared against the Tsai-Wu failure criterion predictions and it was observed that the trained neural network provides a better representation of the experimental data.


DOI
10.12783/asc33/26021

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