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A Methodology for Optimizing Tenon Geometry Dimensions of Mortise-and-Tenon Joint Wood Products
- Source :
- Forests, Vol 12, Iss 478, p 478 (2021), Forests, Volume 12, Issue 4
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- For a long time, the geometry dimensions of tenons have been designed through empirical methods, which is not beneficial to designers and manufacturers and results in more time spent in construction and a greater amount of waste wood materials. In this study, an optimal methodology of combining finite element analysis (FEA) with response surface method (RSM) was proposed to investigate the effect of tenon geometric dimensions (length, width, and thickness) on withdrawal and bending load capacities of mortise-and-tenon (M-T) joints, with the aim of making the design of wood products more scientific. The following results were concluded: (1) the effect of tenon length on withdrawal load capacity was greater than tenon thickness, followed by tenon width<br />(2) the effect of tenon thickness on bending load capacity was greater than those of tenon width, followed by tenon length<br />(3) it was concluded that the tenon length should be designed to be greater than the tenon width and smaller than twice the tenon width, especially, when tenon thickness was relatively thin<br />(4) quadratic models can be used to predict the withdrawal and bending load capacities of M-T joints relating the length, width, and thickness of the tenon<br />(5) the proposed method was capable of being used to optimize the tenon sizes and get more knowledge of M-T joints visually. This study will contribute to reducing the costs of time and materials, and it will result in M-T joints being designed more rationally.
- Subjects :
- 0106 biological sciences
Wood waste
Load capacity
withdrawal
mortise-and-tenon joint
Mortise and tenon
geometric dimensions
Forestry
Geometry
lcsh:QK900-989
02 engineering and technology
Bending
bending
01 natural sciences
Finite element method
010608 biotechnology
lcsh:Plant ecology
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
FEA
Mathematics
Subjects
Details
- ISSN :
- 19994907
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- Forests
- Accession number :
- edsair.doi.dedup.....6c218e5f90753b30165dccc7d0562641