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Application of K-Nearest Neighbors Algorithms for Void Classification in Composite Oriented Strand Board


Title: Application of K-Nearest Neighbors Algorithms for Void Classification in Composite Oriented Strand Board

Authors: Wenyue Hu, Yaser Eftekhari, Sam Callander, Xiaoxing Wang, Christopher C. Bowland, Frank Nguyen, Jeremy McCaslin, Christoph Schaal, Grace X. Gu, Carina Li, Bo Jin

DOI: 10.33599/nasampe/c.23.0117

Abstract: Composite Oriented Strand Board (COSB) is an aerospace-grade material that is manufactured using laminated, unidirectional carbon fiber-epoxy prepreg strands. Its manufacturing methodology allows fine-tuning, producing controllable thickness, flatness, and microstructure with quality assurance. To comprehend the microstructure of COSB from X-ray computed tomography (XCT) scan data, it is critical to accurately assess and quantify void content in the post-analysis. The conventional methods of post-analysis used for X-ray micro-computed tomography (micro-CT) have become inadequate due to their time-consuming nature and oftentimes imprecise measurements. In this paper, we present a new approach for enhancing void classification accuracy by utilizing the K-Nearest Neighbors Classifiers (KNN) with the assistance of convolutional kernels. KNN training based on two labels of greyscale thresholding images achieved a 1% error rate, a significant improvement over the three deep learning algorithms (Fully Convolutional Neural Network, U-net, SegNet) in our previous study. When classifying five different voids labels, the KNN algorithm has an inability to distinguish between labels due to limitations in the feature extraction process, resulting in around a 5% error rate. To overcome these limitations, we propose using convolutional neural networks (CNNs) to make more complex reasoning and decisions when classifying voids, improving the accuracy of void characterization.

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Conference: CAMX 2023

Publication Date: 2023/10/30

SKU: TP23-0000000117

Pages: 13

Price: $26.00

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