A comprehensive evaluation of axial gas permeability in wood using XCT imaging

Zhao, Jun; Li, Lin; Lv, Peiyi; Sun, Zengqi; Cai, Yingchun

doi:10.1007/s00226-022-01449-z

Cited by 1 publication

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These images are then used to reconstruct the object's three-dimensional internal structure by employing tomographic image reconstruction [12]. Select µXCT examples in wood research include moisture swelling and shrinking [13,14], adhesive penetration [15,16], identifying axial gas permeability pathways [17], deformations during tensile adhesive lap-shear specimens [18,19], deformations during three-point bending tests [20], and deformations caused by uniaxial compression [21]. classes of materials to visualize individual structures and quantify their volumes.…”

Section: Introductionmentioning

confidence: 99%

“…In such a volume, segmentation is readily achieved through traditional histogram thresholding techniques [22]. Histogram-based thresholding has been successfully utilized to visualize and quantify wood adhesive flow into wood [15,16,18,19], study swelling and shrinking of wood cell walls [13], and create models to simulate axial permeability [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures

et al. 2023

View full text Add to dashboard Cite

To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials.

show abstract