Quantitative evaluation of rotational wood welding joint strength based on regression of data sets

This study aimed to enhance rotational wood welding technology by developing a simplified prediction model for pull-out strength. The key findings can offer a robust evaluation framework to advance rotational wood welding and expand its applications in woodworking. For instance, (1) A comprehensive database of 689 previously published trials was curated to identify key factors: substrate diameter, effective welded length, and substrate density. (2) Comparative analysis of test outcomes and predictive models revealed consistent trends, suggesting that modeling techniques for self-tapping wood screws could be applied to rotational wood welding joints. (3) Univariate linear regression validated the primary factors, leading to a multivariate model for predicting withdrawal capacity. Theoretical predictions closely matched empirical data, highlighting the model’s industrial applicability.

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Publication Details

Journal
BioResources
Published
2025-02-27
DOI
https://doi.org/10.15376/biores.20.2.2933-2948
Primary Topic
Manufacturing Process and Optimization
Type
article
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article

Quantitative evaluation of rotational wood welding joint strength based on regression of data sets

Pavel Koval, Anatoly Naichuk, Yun Xu, Егор Данилов et al.
BioResources
Manufacturing Process and Optimization
article

Quantitative evaluation of rotational wood welding joint strength based on regression of data sets

Pavel Koval, Anatoly Naichuk, Yun Xu, Егор Данилов, Aleksandr Chernykh, Xuejiao Wang, Roshchina I. Svetlana
article en

Abstract

This study aimed to enhance rotational wood welding technology by developing a simplified prediction model for pull-out strength. The key findings can offer a robust evaluation framework to advance rotational wood welding and expand its applications in woodworking. For instance, (1) A comprehensive database of 689 previously published trials was curated to identify key factors: substrate diameter, effective welded length, and substrate density. (2) Comparative analysis of test outcomes and predictive models revealed consistent trends, suggesting that modeling techniques for self-tapping wood screws could be applied to rotational wood welding joints. (3) Univariate linear regression validated the primary factors, leading to a multivariate model for predicting withdrawal capacity. Theoretical predictions closely matched empirical data, highlighting the model’s industrial applicability.

BioResourcesVol. 20(2)
Brest State Technical University (BY), North China University of Water Resources and Electric Power (CN), Saint Petersburg State University of Architecture and Civil Engineering (RU), Vladimir State University (RU), Zhengzhou Railway Vocational & Technical College (CN)
Openalex Percentile: Top 95%
Manufacturing Process and Optimization
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