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Licensed Unlicensed Requires Authentication Published by De Gruyter March 10, 2022

Prediction model of axially-loaded wood-dowel welding joints by high-speed rotation

Yun Xu ORCID logo, Danying Gao and Aleksandr Grigor’yevich Chernykh
From the journal Holzforschung


Since rotational wood welding was patented in 1997, this technology has been successfully used in the field of timber structures and furniture. A growing body of literature has investigated the mechanical behaviour of wood-dowel welding joints in tension to study the effect of various factors on welding strength. However, up to now, only two predictive approaches were proposed by researchers, and due to the lack of small samples, they were applied in a limited scope. Therefore, this paper aimed to find a way to establish a multivariate model to compute their carrying capacity. A pull-out test database including 1338 trials from previous literature was used as the basis of modelling. Due to the similarity of joints with glued-in-rod, after evaluating its model validation, the modelling method was applied to the welded joints. The data analysis was carried out to determine the primary influences (such as: rotational speed, predrilled hole diameter, welded length and substrate density). Based on the non-linear regression analysis, a multivariate prediction model was proposed, and results showed positive accuracy with measured values. This study provides a new comprehensive assessment of the withdrawal strength of welded joints, and can help identify future technical guidelines within the wood industry.

Corresponding authors: Yun Xu, School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou, 450045, People’s Republic of China, E-mail: ; and Danying Gao, School of Water Conservancy Engineering and Environment, Zhengzhou University, Zhengzhou, 450001, People’s Republic of China, E-mail:

Funding source: Key Scientific Research Project of Henan Province

Award Identifier / Grant number: 192102310222

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 51708217

Funding source: Research Project of Colleges and Universities in Henan Province

Award Identifier / Grant number: 20A580002

Funding source: China Postdoctoral Science Foundation

Award Identifier / Grant number: 2019M652577


The authors would like to thank all people that have made this work possible.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research work was jointly supported by the National Natural Science Foundation of China (grant no. 51708217), China Postdoctoral Science Foundation (grant no. 2019M652577), Key Scientific Research Project of Henan Province (grant no. 192102310222), and Research Project of Colleges and Universities in Henan Province (grant no. 20A580002).

  3. Conflict of interest statement: The authors declare that they have no conflicts of interest regarding this article.


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Received: 2021-03-14
Accepted: 2022-02-09
Published Online: 2022-03-10
Published in Print: 2022-06-27

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