scholarly journals Mechanical behaviour of poly(lactic acid)/cellulose nanocrystal nanocomposites: A comparative study between conventional tensile test and small punch test

2020 ◽  
Vol 14 (12) ◽  
pp. 1127-1136
Author(s):  
S. Singh ◽  
O. Santana-Perez ◽  
C. Rodriguez ◽  
K. Oksman ◽  
M. Ll. Maspoch
Keyword(s):  
Lactic Acid ◽  
Comparative Study ◽  
Tensile Test ◽  
Mechanical Behaviour ◽  
Cellulose Nanocrystal ◽  
Poly Lactic Acid ◽  
Small Punch Test ◽  
Small Punch ◽  
Punch Test

scholarly journals A comparative study of the mechanical, shape-memory, and degradation properties of poly(lactic acid) nanofiber and cellulose nanocrystal reinforced poly(mannitol sebacate) nanocomposites

RSC Advances ◽  
2017 ◽  
Vol 7 (35) ◽  
pp. 21869-21882 ◽  
Author(s):  
Águeda Sonseca ◽  
Olivia Menes ◽  
Enrique Giménez
Keyword(s):  
Lactic Acid ◽  
Comparative Study ◽  
Shape Memory ◽  
Mechanical Behaviour ◽  
Implantable Devices ◽  
Cellulose Nanocrystal ◽  
Poly Lactic Acid ◽  
Degradation Properties

Composites based on elastomeric matrices and with enhanced mechanical behaviour at physiological temperatures useful for shape memory biodegradable implantable devices.

scholarly journals Comparative study between small punch test and hydraulic bulge test

2018 ◽  
Vol 1 (S1) ◽  
pp. 46
Author(s):  
H. Wang ◽  
T. Xu ◽  
J. Zou
Keyword(s):  
Comparative Study ◽  
Bulge Test ◽  
Small Punch Test ◽  
Small Punch ◽  
Punch Test ◽  
Hydraulic Bulge Test ◽  
Hydraulic Bulge

Effect of confinement level on mechanical behaviour using the small punch test

2015 ◽  
Vol 58 ◽  
pp. 206-211 ◽  
Author(s):  
I.I. Cuesta ◽  
C. Rodríguez ◽  
T.E. García ◽  
J.M. Alegre
Keyword(s):  
Mechanical Behaviour ◽  
Small Punch Test ◽  
Small Punch ◽  
Punch Test

scholarly journals Evaluation of Strength Properties of In-Service X80 Pipeline Steel Using Small Punch Test and Backpropagation Neural Network

2021 ◽  
Author(s):  
Ming Song ◽  
Xuyang Li ◽  
Wenchun Jiang ◽  
Jiru Zhong ◽  
Kaishu Guan
Keyword(s):  
Neural Network ◽  
Tensile Test ◽  
Strain Curve ◽  
True Stress ◽  
Small Punch Test ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Small Punch ◽  
Punch Test ◽  
Load Displacement

Abstract Evaluating the strength properties of materials of an in-service pipeline without shutting down transportation has been always a challenge. A novel and non-destructive method for determining the true stress-strain curve of pipeline steel based on backpropagation artificial neural network and small punch test is proposed in this study. The elastoplastic mechanical properties of the pipeline steels could be obtained by this method. The load-displacement curves of 2261 groups of different hypothetical materials were obtained by the finite element model of small punch test within Gurson-Tvergaard-Needleman (GTN) damage parameters and used to train the neural network. The relationship between the load-displacement curve of small punch test and the true stress-strain curve of the conventional uniaxial tensile test was established based on the trained neural network. The accuracy and wide applicability of the trained neural network were verified by the experimental data of four types of materials obtained by small punch test and standard tensile test, respectively. The established relationship can be used to predict the true stress-strain curve of the pipeline steels to determine the elastoplastic mechanical properties only by the load-displacement curve of the small punch test without performing the conventional tensile test.

Prüfung duktiler Werkstoffe mit dem Small-Punch-Test

2005 ◽  
Vol 47 (1-2) ◽  
pp. 45-54 ◽  
Author(s):  
Meinhard Kuna ◽  
Martin Abendroth
Keyword(s):  
Small Punch Test ◽  
Small Punch ◽  
Punch Test
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