Tensile Strength Prediction of Softwood Glulam Lamellas Using Virtual Vibration Technique

2019 ◽  
Vol 31 (12) ◽  
pp. 04019309 ◽  
Author(s):  
A. Khaloian Sarnaghi ◽  
J. W. G. van de Kuilen
Keyword(s):  
Tensile Strength ◽  
Strength Prediction ◽  
Vibration Technique

Tensile strength prediction of natural fiber and natural fiber yarn: Strain rate variation upshot

2020 ◽  
Vol 27 ◽  
pp. 1218-1223
Author(s):  
Sagar Chokshi ◽  
Piyush Gohil ◽  
Amul Lalakiya ◽  
Parth Patel ◽  
Amit Parmar
Keyword(s):  
Tensile Strength ◽  
Strain Rate ◽  
Natural Fiber ◽  
Strength Prediction ◽  
Rate Variation

A stochastic XFEM model for the tensile strength prediction of heterogeneous graphite based on microstructural observations

2017 ◽  
Vol 487 ◽  
pp. 143-157 ◽  
Author(s):  
Manik Bansal ◽  
I.V. Singh ◽  
B.K. Mishra ◽  
Kamal Sharma ◽  
I.A. Khan
Keyword(s):  
Tensile Strength ◽  
Strength Prediction

Comparison of NDE techniques for assessing mechanical properties of unjointed and finger-jointed lumber

Holzforschung ◽  
2011 ◽  
Vol 65 (3) ◽  
Author(s):  
Tobias Biechele ◽  
Ying Hei Chui ◽  
Meng Gong
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Black Spruce ◽  
Correlation Coefficients ◽  
Bending Stiffness ◽  
Strength Prediction ◽  
Finger Joints ◽  
Non Destructive Evaluation ◽  
Non Destructive ◽  
Structural Lumber

Abstract Non-destructive evaluation (NDE) methods are common for grading structural lumber with static bending as the traditional NDE method for strength. More recently, longitudinal and transverse vibration techniques have also been proposed for grading lumber. In this study, unjointed and finger-jointed sawn lumber has been evaluated by these traditional and relatively new NDE methods. In total, 188 pieces of 38 mm×89 mm black spruce lumber were tested. Of these, 40 were unjointed, 47 had 2–3 finger joints, and 101 had 5–7 finger joints. The main objective was to evaluate the reliability of the various NDE techniques in predicting the bending stiffness and tensile strength of finger-jointed lumber with different number of finger joints. Results show that all NDE methods provide stiffness values of unjointed and finger-jointed lumber that correlate well with laboratory measured static bending stiffness with R2 values ranging from 0.76 to 0.97. Moreover, lumber with finger joints has lower bending stiffness than unjointed lumber. Based on the correlation coefficients, there is no evidence that finger joints affect the precision of the strength prediction by NDE methods.

Interface contribution to the SiC-titanium and SiC-aluminium tensile strength prediction

1994 ◽  
Vol 29 (22) ◽  
pp. 6033-6040 ◽  
Author(s):  
L. Molliex ◽  
J. -P. Favre ◽  
A. Vassel ◽  
M. Rabinovitch
Keyword(s):  
Tensile Strength ◽  
Strength Prediction

scholarly journals Tensile strength prediction of orthogonal CFRP under high intensity CW laser irradiation

AIP Advances ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 105304
Author(s):  
Jiawei Wang ◽  
Yongxiang Zhu ◽  
Chenghua Wei ◽  
Yuwei Lv ◽  
Zhiliang Ma ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Laser Irradiation ◽  
High Intensity ◽  
Strength Prediction ◽  
Cw Laser

Tensile strength prediction of dual-phase Al0.6CoCrFeNi high-entropy alloys

2020 ◽  
Vol 27 (10) ◽  
pp. 1341-1346 ◽  
Author(s):  
Min Zhang ◽  
Jin-xiong Hou ◽  
Hui-jun Yang ◽  
Ya-qin Tan ◽  
Xue-jiao Wang ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Strength Prediction ◽  
Dual Phase ◽  
High Entropy Alloys ◽  
High Entropy

Tensile Strength Prediction for Hot Rolled Steels by Bayesian Neural Network Model

2009 ◽  
Vol 42 (23) ◽  
pp. 255-260 ◽  
Author(s):  
Yong Y. Yang ◽  
Mahdi Mahfouf ◽  
Derek A. Linkens ◽  
Qian Zhang
Keyword(s):  
Neural Network ◽  
Tensile Strength ◽  
Network Model ◽  
Neural Network Model ◽  
Strength Prediction ◽  
Bayesian Neural Network ◽  
Hot Rolled
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