Quantitative Evaluation of Rotational Wood Welding Joint Strength Based on Regression of Data Sets

Yun Xu; Xuejiao Wang; Aleksandr G. Chernykh; Roshchina I. Svetlana; Pavel S. Koval; Egor V. Danilov; Anatoly Y. Naichuk

Authors

Yun Xu School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China https://orcid.org/0000-0002-2588-2850
Xuejiao Wang School of International Education, Zhengzhou Railway Vocational and Technical College, Zhengzhou, 451460, China https://orcid.org/0000-0002-1552-6836
Aleksandr G. Chernykh Faculty of Civil Engineering, Saint Petersburg State University of Architecture and Civil Engineering, Saint Petersburg, 190005, Russia https://orcid.org/0000-0001-9805-1428
Roshchina I. Svetlana Institute of Architecture Construction and Energy Engineering, Vladimir State University named after Alexander and Nikolay Stoletovs, 87 Gorky Street, Vladimir, 600000, Russia https://orcid.org/0000-0003-0356-1383
Pavel S. Koval Faculty of Civil Engineering, Saint Petersburg State University of Architecture and Civil Engineering, Saint Petersburg, 190005, Russia https://orcid.org/0000-0003-1911-5169
Egor V. Danilov Faculty of Civil Engineering, Saint Petersburg State University of Architecture and Civil Engineering, Saint Petersburg, 190005, Russia https://orcid.org/0000-0002-8919-4600
Anatoly Y. Naichuk Faculty of Civil Engineering, Brest State Technical University, Brest, 224023, Belarus https://orcid.org/0000-0003-0666-9760

Keywords:

Rotational wood welding, Performance Evaluation, Pull-out strength, Influential factor, Multivariate linear regression analysis

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.

Quantitative Evaluation of Rotational Wood Welding Joint Strength Based on Regression of Data Sets

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