New Perspectives for LVL Manufacturing from Wood of Heterogeneous Quality—Part. 1: Veneer Mechanical Grading Based on Online Local Wood Fiber Orientation Measurement

The grading of wood veneers according to their true mechanical potential is an important issue in the peeling industry. Unlike in the sawmilling industry, this activity does not currently estimate the local properties of production. The potential of the tracheid effect, which enables local fiber orientation measurement, has been widely documented for sawn products. A measuring instrument exploiting this technology and implemented on a peeling line was developed, enabling us to obtain the fiber orientation locally which, together with global density, allowed us to model the local elastic properties of each veneer. A sorting method using this data was developed and is presented here. It was applied to 286 veneers from several logs of French Douglas fir, and was compared to a widely used sorting method based on veneer appearance defects. The effectiveness of both grading approaches was quantified according to mechanical criteria. This study shows that the sorting method used (based on local fiber orientation and average density) allows for better theorical quality discrimination according to the mechanical potential. This article is the first in a series, with the overall aim of enhancing the use of heterogeneous wood veneers in the manufacturing of maximized-performance LVL by veneer grading and optimized positioning as well as material mechanical property modelization.

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

In this paper, we present a deep learning method, DDMReg, for fast and accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. To the best of our knowledge, DDMReg is the first deep-learning-based dMRI registration method. DDMReg is a fully unsupervised method for deformable registration between pairs of dMRI datasets. We propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. We perform comparisons with four state-of-the-art registration methods. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance. In addition, DDMReg leverages deep learning techniques and provides a fast and efficient tool for dMRI registration.

Model Calibration and Data Set Determination Considering the Local Micro-Structure for Short Fiber Reinforced Polymers

To ensure the usability of parts made of fiber-reinforced polymers, a lifetime assessment has to be made in an early stage of the development process. To describe the whole life cycle of these parts, continuous simulation chains can be used. From production to the end of the service life, all influences are mapped virtually. The later material strength is already given after the manufacturing process due to the process dependent fiber alignment. To be able to describe this fiber orientation within the lifetime assessment, this paper presents an approach for model calibration and data set determination to consider the local micro-structure. Therefore, quasi-static and cyclic tests were performed on specimens with longitudinal and transversal fiber orientation. A supplementary failure analysis provides additional information about the local micro-structure. The local fiber orientation is determined with µCT (micro computer tomography)-measurements, correlated to the extraction positions of the specimen, and implemented in a dataset. With an attached lifetime calculation on a demonstrator, a major influence of the local micro-structure on the calculation results can be shown. Therefore, it is indispensable to consider the local fiber orientation in the data set determination of short fiber reinforced polymers.

Evaluating New York raw fiber-to-retail

Inspired by the slow fashion movement, this is an exploratory case study focused on New York’s raw fiber-to-retail value chain for local clothing and textiles. New York has over 470 diverse sheep, alpaca, goat farms, fiber processing mills for product development, and fiber festivals for retail. A survey with farmers presents their motives for establishing a fiber farm business, diverse fibers available, fiber products, income, and their multiple retail venues. Interviews were conducted with farmers, fiber mill owners, and artisan designers. Primary research objectives included: (1) determining how fiber farms, mills, artisans, fiber festivals, and fiber agro-tourism intersect to sustain the current fiber community; (2) evaluating the major challenges the fiber community faces; (3) determining if stakeholders of the fiber community have benefited from any policies; and (4) understanding future goals New York raw fiber-to-fashion stakeholders have to sustain the local fiber community. Interviews reveal several leadership initiatives developed by fiber farmers to address challenges of finding a market, limited income, and fiber mill closures. This study uses the five key dimensions of slow fashion as a framework to evaluate New York raw fiber-to-retail.

Direct Fiber Simulation of a Compression Molded Ribbed Structure Made of a Sheet Molding Compound with Randomly Oriented Carbon/Epoxy Prepreg Strands—A Comparison of Predicted Fiber Orientations with Computed Tomography Analyses

Discontinuous fiber composites (DFC) such as carbon fiber sheet molding compounds (CF-SMC) are increasingly used in the automotive industry for manufacturing lightweight parts. Due to the flow conditions during compression molding of complex geometries, a locally varying fiber orientation evolves. Knowing these process-induced fiber orientations is key to a proper part design since the mechanical properties of the final part highly depend on its local microstructure. Local fiber orientations can be measured and analyzed by means of micro-computed tomography (µCT) and digital image processing, or predicted by process simulation. This paper presents a detailed comparison of numerical and experimental analyses of compression molded ribbed hat profile parts made of CF-SMC with 50 mm long randomly oriented strands (ROS) of chopped unidirectional (UD) carbon/epoxy prepreg tape. X-ray µCT scans of three entire CF-SMC parts are analyzed to compare determined orientation tensors with those coming from a direct fiber simulation (DFS) tool featuring a novel strand generation approach, realistically mimicking the initial ROS charge mesostructure. The DFS results show an overall good agreement of predicted local fiber orientations with µCT measurements, and are therefore precious information that can be used in subsequent integrative simulations to determine the part’s mesostructure-related anisotropic behavior under mechanical loads.