A method for generating finite element models of wood boards from X-ray computed tomography scans

Huber, Johannes Albert Josef; Broman, Olof; Ekevad, Mats; Oja, Johan; Hansson, Lars

doi:10.1016/j.compstruc.2021.106702

Cited by 25 publications

(17 citation statements)

References 48 publications

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“… This dataset can further be used in probabilistic structural analyses of timber-related systems where the definition of the variability in the mechanical properties of wood in both parallel and perpendicular load-to-grain directions is paramount. This dataset can be reused for comparison with data obtained from other non-destructive techniques for testing and modelling of wood such as ultrasounds [3] , or X-ray computed tomography scans [4] . …”

Section: Value Of the Datamentioning

confidence: 99%

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Experimental campaign on the mechanical properties of Canadian small clear spruce-pine-fir wood: Experimental procedures, data curation, and data description

Loss

2023

Data in Brief

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Section: Value Of the Datamentioning

confidence: 99%

“…This dataset can be reused for comparison with data obtained from other non-destructive techniques for testing and modelling of wood such as ultrasounds [3] , or X-ray computed tomography scans [4] .…”

Section: Value Of the Datamentioning

confidence: 99%

Experimental campaign on the mechanical properties of Canadian small clear spruce-pine-fir wood: Experimental procedures, data curation, and data description

Loss

2023

Data in Brief

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“…174−178 For instance, Hassani et al introduced a rheological model for wood, 179 and Harrington 180 modeled the hygroelastic properties of softwood. Huber et al 181 developed a method for generating finite element models of wood boards from X-ray computed tomography scans, and Yang and colleagues generated a two-dimensional lattice model for simulating the failure and fracture behavior of wood. 176 However, because the individual components of wood, the hierarchical structure, and the interaction with the environment behave nonlinearly, one can safely assume that wood falls under the category of general complexity.…”

Section: Managing Wood Complexity With Machine Learning 61 Wood As a ...mentioning

confidence: 99%

“…By using either only one wood species (in Europe, mainly Norway spruce wood), small sample sizes, or already sorted wood (free of knots and defects), it was possible to “linearize” the problem and to generate with success deterministic physical models. − For instance, Hassani et al introduced a rheological model for wood, and Harrington modeled the hygroelastic properties of softwood. Huber et al developed a method for generating finite element models of wood boards from X-ray computed tomography scans, and Yang and colleagues generated a two-dimensional lattice model for simulating the failure and fracture behavior of wood …”

Section: Managing Wood Complexity With Machine Learningmentioning

confidence: 99%

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

2022

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Wood is a renewable resource with excellent qualities and the potential to become a key element of a future bioeconomy. The increasing environmental awareness and drive to achieve sustainability is leading to a resurgence of research on wood materials. Nevertheless, the global climate changes and associated consequences will soon challenge the wood-value chains in several regions (e.g., central Europe). To cope with these challenges, it is necessary to rethink the current practice of wood sourcing and transformation. The goal of this review is to address the intrinsic natural diversity of wood, from its origin to its technological consequences for the present and future manufacturing of wood products. So far, industrial processes have been optimized to repress the variability of wood properties, enabling more efficient processing and production of reliable products. However, the need to preserve biodiversity and the impact of climate change on forests call for new wood processing techniques and green chemistry protocols for wood modification as enabling factors necessary for managing a more diverse wood provision in the future. This article discusses the past developments that have resulted in the current wood value chains and provides a perspective about how natural variability could be turned into an asset for making truly sustainable wood products. After briefly introducing the chemical and structural complexity of wood, the methods conventionally adopted for industrial homogenization and modification of wood are discussed in relation to their evolution toward increased sustainability. Finally, a perspective is given on technological potentials of machine learning techniques and of novel functional wood materials. Here the main message is that through a combination of sustainable forestry, adherence to green chemistry principles and adapted processes based on machine learning, the wood industry could not only overcome current challenges but also thrive in the near future despite the awaiting challenges.

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“…The history of macro-CT-aided FE modelling of wood is brief. Pyrkosz et al (2010) first used the method to obtain the most dominant vibration mode of a Stradivari violin, while Coaldrake (2020) identified the different vibration modes of a traditional Japanese harp, and Hartig et al (2021) and Huber et al (2022) used the method to validate the elastic behaviour of moulded wood products and timber, respectively. Pang and Wiberg (1998), Eriksson et al (2007) and Li et al (2018) have also actively used macro-CT data in combination with FE modelling.…”

Section: Introductionmentioning

confidence: 99%

Macroscopic X-ray computed tomography aided numerical modelling of moisture flow in sawn timber

et al. 2022

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Mathematical models are essential for the development of schedules for the air-circulation drying of timber in Swedish sawmills, but earlier models have been shown to be conservative leading to longer drying times than necessary. In the current study, macroscopic (macro) X-ray computed tomography (CT) has been used in both the development and validation of a finite element (FE) model, to enable the macro-CT aided FE modelling of the nonlinear transient moisture flow in wood. The model uses more advanced theory than has previously been used in Swedish sawmills, by incorporating a surface emission coefficient to simulate the surface resistance to moisture flow. A single piece of Norway spruce [Picea abies (L.) Karst.] timber was subjected to that part of a traditional kiln-drying schedule, which is associated with diffusion-driven moisture transport. The incorporation of macro-CT data into the FE model resulted in a more realistic representation of the board’s geometry, the initial moisture state, and the definition of material parameters. It also led to a better simulation of flow speed and moisture gradient, especially the asymmetric MC development within the cross section throughout the drying process.

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A method for generating finite element models of wood boards from X-ray computed tomography scans

Cited by 25 publications

References 48 publications

Experimental campaign on the mechanical properties of Canadian small clear spruce-pine-fir wood: Experimental procedures, data curation, and data description

Experimental campaign on the mechanical properties of Canadian small clear spruce-pine-fir wood: Experimental procedures, data curation, and data description

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

Macroscopic X-ray computed tomography aided numerical modelling of moisture flow in sawn timber

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