The effect of elevated temperature exposure on the fracture toughness of solid wood and structural wood composites

This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Springer and can be found at: http://www.springer.com/life+sciences/forestry/journal/226.Fracture toughness of wood and wood composites has traditionally been characterized by a stress intensity factor, an initiation strain energy release rate (G[subscript init]) or a total energy to fracture (G[subscript f]). These parameters provide incomplete fracture characterization for these materials because the toughness changes as the crack propagates. Thus for materials such as wood, oriented strand board (OSB), plywood and laminated veneer lumber (LVL), it is essential to characterize the fracture properties during crack propagation by measuring a full crack resistant or R curve. This study used energy methods during crack propagation to measure full R curves and then compared the fracture properties of wood and various wood-based composites such as, OSB, LVL and plywood. The effect of exposure to elevated temperature on fracture properties of these materials was also studied. The steady state energy release rate (G[subscript SS]) of wood was lower than that of wood composites such as LVL, plywood and OSB. The resin in wood composites provides them with a higher fracture toughness compared to solid lumber. Depending upon the internal structure of the material the mode of failure also varied. With exposure to elevated temperatures, G[subscript SS] for all materials decreased while the failure mode remained the same. The scatter associated with conventional bond strength tests, such as internal bond (IB) and bond classification tests, renders any statistical comparison using those tests difficult. In contrast, fracture tests with R curve analysis may provide an improved tool for characterization of bond quality in wood composites

Image analysis to assess wood variability in longleaf pine cross-sectional disks

Image analysis is an important method for rapidly measuring wood property variation, but it is infrequently applied to disks collected from forestry studies. The objective of this study was to compare image estimated wood and bark volumes and diameters to reference measurements, and to extract more information from the images including the shape (out of round index, eccentric pith) and the amount and location of severe compression wood. A total of 1,120 disks were cut from multiple height levels of 48 defect-free and 56 defect-containing longleaf pine (Pinus palustris) trees from 16 stands across Georgia (U.S.). Disks were machined on one transverse surface using a computer numeric controlled router to prepare a clean surface for imaging. Three images; one under white light, second under blue light, and third under blue light with a green longpass filter, were taken for each disk. Volumes and diameters estimated from images were in close agreement with reference methods. Linear models fitted as measured versus image volumes for wood and bark had coefficient of determination (R2) values of >0.99 and 0.96. Linear models fitted as measured versus image diameters had R2 values of >0.99. Out of round index and pith eccentricity values calculated from images showed a moderate positive correlation (R=0.43). Algorithms developed were able to correctly identify severe compression wood, but not mild to moderate compression wood. Severe compression wood was moderately correlated to out of round index (R=0.54) and pith eccentricity (R=0.48). More than 98% of the disks having severe compression wood came from defect-containing trees

Acoustic emission monitoring of wood materials and timber structures: A critical review

The growing interest in timber construction and using more wood for civil engineering applications has given highlighted importance of developing non-destructive evaluation (NDE) methods for structural health monitoring and quality control of wooden construction. This study, critically reviews the acoustic emission (AE) method and its applications in the wood and timber industry. Various other NDE methods for wood monitoring such as infrared spectroscopy, stress wave, guided wave propagation, X-ray computed tomography and thermography are also included. The concept and experimentation of AE are explained, and the impact of wood properties on AE signal velocity and energy attenuation is discussed. The state-of-the-art AE monitoring of wood and timber structures is organized into six applications: (1) wood machining monitoring; (2) wood drying; (3) wood fracture; (4) timber structural health monitoring; (5) termite infestation monitoring; and (6) quality control. For each application, the opportunities that the AE method offers for in-situ monitoring or smart assessment of wood-based materials are discussed, and the challenges and direction for future research are critically outlined. Overall, compared with structural health monitoring of other materials, less attention has been paid to data-driven methods and machine learning applied to AE monitoring of wood and timber. In addition, most studies have focused on extracting simple time-domain features, whereas there is a gap in using sophisticated signal processing and feature engineering techniques. Future research should explore the sensor fusion for monitoring full-scale timber buildings and structures and focus on applying AE to large-size structures containing defects. Moreover, the effectiveness of AE methods used for wood composites and mass timber structures should be further studied

Acoustic emission monitoring of wood materials and timber structures: A critical review

The growing interest in timber construction and using more wood for civil engineering applications has given highlighted importance of developing non-destructive evaluation (NDE) methods for structural health monitoring and quality control of wooden construction. This study, critically reviews the acoustic emission (AE) method and its applications in the wood and timber industry. Various other NDE methods for wood monitoring such as infrared spectroscopy, stress wave, guided wave propagation, X-ray computed tomography and thermography are also included. The concept and experimentation of AE are explained, and the impact of wood properties on AE signal velocity and energy attenuation is discussed. The state-of-the-art AE monitoring of wood and timber structures is organized into six applications: (1) wood machining monitoring; (2) wood drying; (3) wood fracture; (4) timber structural health monitoring; (5) termite infestation monitoring; and (6) quality control. For each application, the opportunities that the AE method offers for in-situ monitoring or smart assessment of wood-based materials are discussed, and the challenges and direction for future research are critically outlined. Overall, compared with structural health monitoring of other materials, less attention has been paid to data-driven methods and machine learning applied to AE monitoring of wood and timber. In addition, most studies have focused on extracting simple time-domain features, whereas there is a gap in using sophisticated signal processing and feature engineering techniques. Future research should explore the sensor fusion for monitoring full-scale timber buildings and structures and focus on applying AE to large-size structures containing defects. Moreover, the effectiveness of AE methods used for wood composites and mass timber structures should be further studied

Quality of tectona grandis for sawn wood production

Forestry companies have invested in genetic improvement to increase wood production in a shorter amount of time. Thus, studies are needed to compare the properties of clonal and seminal wood materials.  The objective of this study was to analyze physical and mechanical properties of Tectona grandis from clonal (C1 and C2) and seminal (S) origin and evaluate the yield and quality of sawn wood subjected to outdoor and oven drying. Genetic material was collected from six, 15-year-old trees. Clone C2 presented the lowest amount of bark, and 51 % heartwood up to half the commercial height, while the heartwood of C1 and S went up to 25 % of the height. The three materials did not differ statistically for maximum angular deviation, pith eccentricity, basic density, Janka hardness, anisotropy, commercial income of sawn wood and the presence of knots. After the drying processes, the bowing and crooking indexes were less than 5 mm.m-1, however, the seminal material showed a higher cracking incidence after outdoor and oven drying. In conclusion, the wood properties of the three materials are similar. In addition, the oven drying process is recommended

Special Libraries, March 1943

Volume 34, Issue 3https://scholarworks.sjsu.edu/sla_sl_1943/1002/thumbnail.jp

Computer vision-based monitoring of abrasive loading during wood machining

Surface quality is an important characteristic commonly assessed in wooden products. Sanding relies on coated abrasives as tooling for both dimensioning and surface finishing but their performance is dependent on chip loading and grit wear. Traditionally, the useful life of abrasive belts in sanding operation has been manually assessed. This type of inspection is highly subjective and dependent upon individual expertise, consequently leading to under utilization or over utilization of the abrasive. This, in turn, affects the production costs and quality of the product. In this work, an intelligent classification method that determines the optimal replacement policy for a belt exposed to known manufacturing parameters is developed. Controlled experiments were conducted to develop abrasive belts of known exposure, followed with digital microscopy to capture images and process them with pattern recognition and classification algorithms. Grit size and machining time were the parameters of interest while response of the experiments included image information from the abrasive sheets after every experimental run. These images were used in training an artificial neural network that in turn, help in determining data to categorize the useful life of the abrasive. The results show a 95% success rate in accurately classifying abrasive images of similarly conditioned abrasives. Also, the results show that the classification of interpolated and extrapolated times of abrasive usage are classified with a 95% success rate. A classification of abrasive images is proposed to be used as one of the inputs to a decision system that would help in evaluating the life of the abrasive and replacement policies. Further research on the relationship between the different parameters affecting the useful life of the abrasive is proposed