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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 void labels, the multi-classifier KNN algorithm with convolutional kernels yields around 2% error rate. Encouraged by these results, we propose using convolutional neural networks (CNNs) to make more complex reasoning and decisions when classifying voids, improving the accuracy of void characterization.

Original languageEnglish
Title of host publicationComposites and Advanced Materials Expo, CAMX 2023
PublisherThe Composites and Advanced Materials Expo (CAMX)
ISBN (Electronic)9781934551448
DOIs
StatePublished - 2023
Event9th Annual Composites and Advanced Materials Expo, CAMX 2023 - Atlanta, United States
Duration: Oct 30 2023Nov 2 2023

Publication series

NameComposites and Advanced Materials Expo, CAMX 2023

Conference

Conference9th Annual Composites and Advanced Materials Expo, CAMX 2023
Country/TerritoryUnited States
CityAtlanta
Period10/30/2311/2/23

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