Sandhya Samarasinghe, Don Kulasiri, Tristan Jamieson. (2007). Neural networks for predicting fracture toughness of individual wood samples. Silva Fennica vol. 41 no. 1 article id 309. https://doi.org/10.14214/sf.309

Keywords: Pinus radiata; New Zealand; video imaging; strain energy release rate; Neural Networks; fracture toughness

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Strain energy release rate (G_Ic) of Pinus radiata in the TL opening mode was determined using the compliance crack length relationship. A total of 123 specimens consisting of four sizes of specimen with each size having four different crack lengths were tested. For each specimen, grain and ring angles, density and moisture content were measured. Video imaging, was used to measure crack length during propagation. Since cracks extended in stages, full compliance-crack length relationship was developed for each specimen based on their initial and subsequent crack lengths. No significant differences in G_Ic, between initial and subsequent crack lengths were found for the smaller specimens by paired sample t-tests, but differences were significant for the largest specimen size. The Average fracture toughness was calculated from G_Ic and it was 215 kPa.m^0.5. Three artificial neural networks were developed to predict the: 1) force required to propagate a crack, 2) crack extension, and 3) fracture toughness of an individual specimen. Each was successful, producing respective R² of 0.870, 0.865, and 0.621 on validation data. A sensitivity analysis of the networks revealed that the crack length was the most influential with 21% contribution followed by grain angle with 14% contribution for predicting the applied force. This was followed by volume and physical properties. For predicting the crack extension, density had the greatest contribution (20%) followed by previous crack length and force contributing 16% equally. Fracture toughness was dominated by the dimensional parameters of the specimen contributing (42%) followed by anisotropy and physical properties.

Samarasinghe, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: ss@nn.nz
Kulasiri, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: dk@nn.nz
Jamieson, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: tj@nn.nz

article id 415, category Research article

Sandhya Samarasinghe, Don Kulasiri. (2004). Stress intensity factor of wood from crack-tip displacement fields obtained from digital image processing. Silva Fennica vol. 38 no. 3 article id 415. https://doi.org/10.14214/sf.415

Keywords: Pinus radiata; wood; fracture toughness; stress intensity factor; digital image correlation; orthotropic fracture theory

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Stress intensity factor of radiata pine (Pinus radiata) in Tangential-Longitudinal opening mode was determined from crack-tip displacement fields obtained from digital image correlation in conjunction with orthotropic fracture theory. For lower loads, experiments agreed with the linear elastic fracture theory but for higher loads, stress intensity factor and load relationship was nonlinear. For 41% of the specimens tested, tip-displacement based stress intensity factor agreed with that based on the ASTM standard formula for lower loads but deviated for higher loads closer to failure. The tip displacement plots showed that the nonlinear behaviour is due to large displacements which we attributed to large plastic deformations and/or micro-cracking in this region. The other 59% specimens showed a similar trend except that the crack-tip based stress intensity factor was consistently higher than the value obtained from the standard formula. The fracture toughness from tip displacements was larger than the standard values for all specimens and the two were related by a logarithmic function with an R2 of 0.61. The study also established that fracture toughness increases with the angle of inclination of the original crack plane to the Radial Longitudinal plane.

Samarasinghe, Lincoln University, P.O. Box 84, Canterbury, New Zealand E-mail: ss@nn.nz
Kulasiri, Lincoln University, P.O. Box 84, Canterbury, New Zealand E-mail: kulasird@lincoln.ac.nz

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