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

García, Ángela; Anjos, O.; Iglesias, Carla; Pereira, Helena; Martínez, Javier; Taboada, Javier

http://hdl.handle.net/10400.11/5612

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Abstract(s)

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.

Keywords

Cork Mechanical properties Neural network Multiple linear regression CART Cluster

URI

http://hdl.handle.net/10400.11/5612

Citation

GARCÍA, A. [et al.] (2015) - Prediction of mechanical strength of cork under compression using machine learning techniques. Materials and Design. ISSN 0261-3069. 82. P. 304-311.