Best Practices for Quasistatic Berkovich Nanoindentation of Wood Cell Walls

For wood and forest products to reach their full potential as structural materials, experimental techniques are needed to measure mechanical properties across all length scales. Nanoindentation is uniquely suited to probe in situ mechanical properties of micrometer-scale features in forest products, such as individual wood cell wall layers and adhesive bondlines. However, wood science researchers most commonly employ traditional nanoindentation methods that were originally developed for testing hard, inorganic materials, such as metals and ceramics. These traditional methods assume that the tested specimen is rigidly supported, homogeneous, and semi-infinite. Large systematic errors may affect the results when these traditional methods are used to test complex polymeric materials, such as wood cell walls. Wood cell walls have a small, finite size, and nanoindentations can be affected by nearby edges. Wood cell walls are also not rigidly supported, and the cellular structure can flex under loading. Additionally, wood cell walls are softer and more prone to surface detection errors than harder inorganic materials. In this paper, nanoindentation methods for performing quasistatic Berkovich nanoindentations, the most commonly applied nanoindentation technique in forest products research, are presented specifically for making more accurate nanoindentation measurements in materials such as wood cell walls. The improved protocols employ multiload nanoindentations and an analysis algorithm to correct and detect errors associated with surface detection errors and structural compliances arising from edges and specimen-scale flexing. The algorithm also diagnoses other potential issues arising from dirty probes, nanoindenter performance or calibration issues, and displacement drift. The efficacy of the methods was demonstrated using nanoindentations in loblolly pine (Pinus taeda) S2 cell wall layers (S2) and compound corner middle lamellae (CCML). The nanoindentations spanned a large range of sizes. The results also provide new guidelines about the minimum size of nanoindentations needed to make reliable nanoindentation measurements in S2 and CCML.

Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset

Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy.