Prediction of mechanical strength of cork under compression using machine learning techniques

• Machine learning techniques are used to predict mechanical behaviour of cork.
• Compressive stress at 30% strain can be predicted with neural networks.
• Heterogeneity of cork complicates the prediction of some mechanical properties.

In this study, the accuracy of mathematical techniques such as multiple linear regression, clustering, decision trees (CART) and neural networks was evaluated to predict Young’s modulus, compressive stress at 30% strain and instantaneous recovery velocity of cork. Physical properties, namely test direction, density, porosity and pore number, as well as test direction were used as input. The better model was achieved when a classification problem was performed. Only compressive stress at 30% strain can be predicted with neural networks with an error rate of about 20%. The prediction of Young’s modulus and instantaneous recovery velocity led to unacceptably high error rates due to the heterogeneity of the material.

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