文献类型：期刊文献

英文题名：Image-Based Evaluation of Cracking Degrees on Wood Fiber Bundles: A Machine Learning Approach

作者：Chai, Zheming[1] Liu, Heng[1] Guo, Haomeng[1] Xu, Jinmei[1] Yu, Yanglun[1] Yang, Jianhua[1]

第一作者：Chai, Zheming

通信作者：Yang, JH[1]

机构：[1]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China

年份：2024

卷号：15

期号：4

外文期刊名：FORESTS

收录：;EI(收录号：20241815998205);Scopus(收录号：2-s2.0-85191398954);WOS:【SCI-EXPANDED(收录号:WOS:001210682700001)】；

基金：The author wishes to convey heartfelt thanks to Zhang Leping for her outstanding guidance in the field of data visualization. Additionally, heartfelt appreciation is extended to Feng Haiyun, Chen Sihan, and Liu Zixin for their unwavering support and invaluable help throughout the process of writing this paper.

语种：英文

外文关键词：wood scrimber; wood fiber bundles; cracking degree evaluation; crack image analysis; water absorption rate prediction; machine learning

摘要：In this study, a machine learning-based method to assess and predict the cracking degree (CD) on wood fiber bundles (WFB) was developed, which is crucial for enhancing the quality control and refining the production process of wood scrimber (WS). By roller-cracking poplar wood one to three times, three distinct CD levels were established, and 361 WFB specimens were analyzed, using their water absorption rate (WAR) as the foundation for CD prediction. Through crack image analysis, four key quantitative parameters were identified-cracking density, coherence degree, crack count, and average width-and this study found through discriminant analysis that the discrimination accuracy on the CD levels by cracking density or coherence degree over 90%, emphasizing their significance in evaluation. Cluster analysis grouped the specimens into three clusters based on four key quantitative parameters, aligning with the CD levels. This study developed specialized prediction models for each CD level, integrating principal component analysis for dimensionality reduction with polynomial fitting, achieving mean squared error (MSE) of 0.0132, 0.0498, and 0.0204 for levels 1, 2, and 3, respectively. An integrated model, with an accuracy of 94.3% and predictions within a 20% error margin, was created, demonstrating the effectiveness of using surface crack image features to predict WAR of WFB. This research establishes a methodological framework for assessing CDs on WFB, contributing to enhancing WS product quality and helping to better understand wood cracking and water absorption mechanisms.