scholarly journals Evaluation of the stress–strain curve of metallic materials by spherical indentation

2006 ◽  
Vol 43 (7-8) ◽  
pp. 2441-2459 ◽  
Author(s):  
M. Beghini ◽  
L. Bertini ◽  
V. Fontanari
Keyword(s):  
Strain Curve ◽  
Spherical Indentation ◽  
Metallic Materials ◽  
Stress Strain Curve ◽  
Stress Strain

Determining engineering stress–strain curve directly from the load–depth curve of spherical indentation test

2010 ◽  
Vol 25 (12) ◽  
pp. 2297-2307 ◽  
Author(s):  
Baoxing Xu ◽  
Xi Chen
Keyword(s):  
Indentation Test ◽  
Strain Curve ◽  
Spherical Indentation ◽  
Displacement Curve ◽  
Large Set ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Constraint Factors ◽  
Engineering Stress ◽  
Depth Curve

The engineering stress–strain curve is one of the most convenient characterizations of the constitutive behavior of materials that can be obtained directly from uniaxial experiments. We propose that the engineering stress–strain curve may also be directly converted from the load–depth curve of a deep spherical indentation test via new phenomenological formulations of the effective indentation strain and stress. From extensive forward analyses, explicit relationships are established between the indentation constraint factors and material elastoplastic parameters, and verified numerically by a large set of engineering materials as well as experimentally by parallel laboratory tests and data available in the literature. An iterative reverse analysis procedure is proposed such that the uniaxial engineering stress–strain curve of an unknown material (assuming that its elastic modulus is obtained in advance via a separate shallow spherical indentation test or other established methods) can be deduced phenomenologically and approximately from the load–displacement curve of a deep spherical indentation test.

scholarly journals Determination of Plastic Properties of Polycrystalline Metallic Materials by Nanoindentation – Experiments and Finite Element Simulations

2004 ◽  
Vol 841 ◽  
Author(s):  
Karsten Durst ◽  
Björn Backes ◽  
Mathias Göken
Keyword(s):  
Finite Element ◽  
Strain Curve ◽  
Finite Element Simulations ◽  
Metallic Materials ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Plastic Properties ◽  
Ultrafine Grained ◽  
Indentation Tests

ABSTRACTThe determination of plastic properties of metallic materials by nanoindentation requires the analysis of the indentation process and the evaluation methods. Particular effects on the nanoscale, like the indentation size effect or piling up of the material around the indentation, need to be considered. Nanoindentation experiments were performed on conventional grain sized (CG) as well as on ultrafine-grained (UFG) copper and brass. The indentation experiments were complemented with finite element simulations using the monotonic stress-strain curve as input data. All indentation tests were carried out using cube-corner and Berkovich geometry and thus different amount of plastic strain was applied to the material, according to Tabors theory. We find an excellent agreement between simulations and experiments for the UFG materials from which a representative strain of εB ≈ 0.1 and εcc ≈ 0.2 is determined. With these data, the slope of the stress-strain curve is predicted for all materials down to an indentation depth of 800 nm.

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.

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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