201 Stress-strain curve estimation of anisotropic plastic materials using indentation method

2010 ◽  
Vol 2010.85 (0) ◽  
pp. _2-1_
Author(s):  
Keishi YONEDA ◽  
Akio YONEZU ◽  
Masayuki SAKIHARA ◽  
Hiroyuki HIRAKATA ◽  
Koji MINOSHIMA
Keyword(s):  
Strain Curve ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Indentation Method ◽  
Curve Estimation ◽  
Plastic Materials ◽  
Anisotropic Plastic

Towards the Problem of Construction an SPD Stress-Strain Curve for Low-Plastic Materials

2020 ◽  
Vol 839 ◽  
pp. 189-195
Author(s):  
Pavel G. Morrev ◽  
Kostya I. Kapyrin ◽  
I.M. Gryadunov ◽  
Sergey Y. Radchenko ◽  
Daniil O. Dorokhov ◽  
...  
Keyword(s):  
Plastic Deformation ◽  
Shear Band ◽  
Severe Plastic Deformation ◽  
Initial Segment ◽  
Large Strain ◽  
Strain Curve ◽  
Deep Rolling ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Plastic Materials

Stress-strain curve construction for low-plastic alloys under severe plastic deformation conditions is considered. A material under investigation is cast bronze Cu85-Pb5-Sn5-Zn5. Experiments on upsetting and deep rolling were conducted. Based on these data, the initial hardening modular and the hardening modular at large strain were evaluated. Classic tests on determining an initial segment of stress-strain curve can lead to grate mistakes because shear band sliding can diminishes appreciably both yield stress and hardening modular. A correct methodology for stress-strain curve construction is proposed.

scholarly journals Representative Stress-Strain Curve by Spherical Indentation on Elastic-Plastic Materials

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Chang ◽  
M. A. Garrido ◽  
J. Ruiz-Hervias ◽  
Zhu Zhang ◽  
Le-le Zhang
Keyword(s):  
Strain Curve ◽  
Stress Constraint ◽  
Spherical Indentation ◽  
Stress Strain Curve ◽  
Constraint Factor ◽  
Stress Strain ◽  
Constraint Factors ◽  
Plastic Materials ◽  
Elastic Plastic Materials ◽  
The Relationship

Tensile stress-strain curve of metallic materials can be determined by the representative stress-strain curve from the spherical indentation. Tabor empirically determined the stress constraint factor (stress CF), ψ, and strain constraint factor (strain CF), β, but the choice of value for ψ and β is still under discussion. In this study, a new insight into the relationship between constraint factors of stress and strain is analytically described based on the formation of Tabor’s equation. Experiment tests were performed to evaluate these constraint factors. From the results, representative stress-strain curves using a proposed strain constraint factor can fit better with nominal stress-strain curve than those using Tabor’s constraint factors.

Improving elastic modulus measurements for rock based on geology

2000 ◽  
Vol 6 (4) ◽  
pp. 333-346 ◽  
Author(s):  
Paul M. Santi ◽  
Jason E. Holschen ◽  
Richard W. Stephenson
Keyword(s):  
Elastic Modulus ◽  
Strain Curve ◽  
Elastic Behavior ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Rock Types ◽  
Plastic Materials ◽  
Straight Line ◽  
Elastic Plastic Materials ◽  
Engineering Projects

Abstract Since many engineering projects in rock never mobilize strengths near the uniaxial compressive strength (UCS) of the rock, elastic modulus becomes a critical parameter to describe the rock's behavior under loading. There are a number of methods available for calculating the elastic modulus from laboratory test data, and each method gives a slightly different value. The objective of this study is to evaluate the most repeatable method for each of a number of rock types, and then to develop guidelines to aid the practitioner in selecting the best method as a function of rock behavior. UCS tests were performed on 78 samples of nine rock types, including two basalts, two granites, two limestones, a quartzite, a sandstone, and a gypsum. Elastic moduli were calculated using six different methods reported in the literature or modified for this study. The modified secant and modified secant-at-50-percent-strength moduli (modified by shifting the origin to best intercept the extension of the main straight-line portion of the stress-strain curve) were the most repeatable methods for rocks with elastic and plastic-elastic behavior. Elastic-plastic materials, which have a broad concave-downward stress-strain curve, are best evaluated using the tangent modulus on the upper of two distinct straight-line segments. For materials which show creep or extended plastic deformation with no sharp failure, the secant-at-50-percent-strength modulus and modified secant-at-50-percent-strength modulus are the most repeatable.

A modified version of MATLAB application window for predicting the weld bead profile and stress–strain plot of AA5052 CMT weldment using ER4043

SIMULATION ◽  
2021 ◽  
pp. 003754972110315
Author(s):  
B Girinath ◽  
N Siva Shanmugam
Keyword(s):  
Strain Curve ◽  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Hybrid Technique ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Weld Bead Geometry ◽  
Inference System ◽  
Engineering Stress

The present study deals with the extended version of our previous research work. In this article, for predicting the entire weld bead geometry and engineering stress–strain curve of the cold metal transfer (CMT) weldment, a MATLAB based application window (second version) is developed with certain modifications. In the first version, for predicting the entire weld bead geometry, apart from weld bead characteristics, x and y coordinates (24 from each) of the extracted points are considered. Finally, in the first version, 53 output values (five for weld bead characteristics and 48 for x and y coordinates) are predicted using both multiple regression analysis (MRA) and adaptive neuro fuzzy inference system (ANFIS) technique to get an idea related to the complete weld bead geometry without performing the actual welding process. The obtained weld bead shapes using both the techniques are compared with the experimentally obtained bead shapes. Based on the results obtained from the first version and the knowledge acquired from literature, the complete shape of weld bead obtained using ANFIS is in good agreement with the experimentally obtained weld bead shape. This motivated us to adopt a hybrid technique known as ANFIS (combined artificial neural network and fuzzy features) alone in this paper for predicting the weld bead shape and engineering stress–strain curve of the welded joint. In the present study, an attempt is made to evaluate the accuracy of the prediction when the number of trials is reduced to half and increasing the number of data points from the macrograph to twice. Complete weld bead geometry and the engineering stress–strain curves were predicted against the input welding parameters (welding current and welding speed), fed by the user in the MATLAB application window. Finally, the entire weld bead geometries were predicted by both the first and the second version are compared and validated with the experimentally obtained weld bead shapes. The similar procedure was followed for predicting the engineering stress–strain curve to compare with experimental outcomes.

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