A Correction to the Stress–Strain Curve During Multistage Hot Deformation of 7150 Aluminum Alloy Using Instantaneous Friction Factors

2018 ◽  
Vol 27 (6) ◽  
pp. 3083-3090 ◽  
Author(s):  
Fulin Jiang ◽  
Jie Tang ◽  
Dinfa Fu ◽  
Jianping Huang ◽  
Hui Zhang
Keyword(s):  
Aluminum Alloy ◽  
Hot Deformation ◽  
Strain Curve ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Friction Factors

scholarly journals Identifying the stress–strain curve of materials by microimpact testing. Application on pure copper, pure iron, and aluminum alloy 6061-T651

2015 ◽  
Vol 30 (14) ◽  
pp. 2222-2230 ◽  
Author(s):  
Halim Al Baida ◽  
Cécile Langlade ◽  
Guillaume Kermouche ◽  
Ricardo Rafael Ambriz
Keyword(s):  
Aluminum Alloy ◽  
Pure Iron ◽  
Pure Copper ◽  
Strain Curve ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Aluminum Alloy 6061 ◽  
Image Position ◽  
Alloy 6061

Abstract

Stress-Strain Behavior of an Aluminum Alloy Under Transient Strain-Rates

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Y. W. Kwon ◽  
Y. Esmaeili ◽  
C. M. Park
Keyword(s):  
Aluminum Alloy ◽  
Strain Rate ◽  
Fracture Strain ◽  
Strain Curve ◽  
Strain Rates ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Single Strain ◽  
Transient Strain ◽  
Strain Rate Loading

Because most structures are subjected to transient strain-rate loading, an experimental study was conducted to investigate the stress-strain behaviors of an aluminum alloy undergoing varying strain-rate loading. To this end, uniaxial tensile loading was applied to coupons of dog-bone shape such that each coupon underwent two or three different strain-rates, i.e., one rate after another. As a basis, a series of single-strain-rate tests was also conducted with strain-rates of 0.1–10.0 s−1. When the material experienced multistrain-rate loading, the stress-strain curves were significantly different from any single-strain-rate stress-strain curve. The strain-rate history affected the stress-strain curves under multistrain-rate loading. As a result, some simple averaging of single-strain-rate curves did not predict the actual multistrain-rate stress-strain curve properly. Furthermore, the fracture strain under multistrain-rate loading was significantly different from that under any single-strain-rate case. Depending on the applied strain-rates and their sequences, the former was much greater or less than the latter. A technique was proposed based on the residual plastic strain and plastic energy density in order to predict the fracture strain under multistrain-rate loading. The predicted fracture strains generally agreed well with the experimental data. Another observation that was made was that the unloading stress-strain curve was not affected by the previous strain-rate history.

scholarly journals Estimation of Texture-Dependent Stress-Strain Curve and r-Value of Aluminum Alloy Sheet Using Deep Learning

2020 ◽  
Vol 61 (12) ◽  
pp. 2276-2283 ◽  
Author(s):  
Kohta Koenuma ◽  
Akinori Yamanaka ◽  
Ikumu Watanabe ◽  
Toshihiko Kuwabara
Keyword(s):  
Aluminum Alloy ◽  
Deep Learning ◽  
Strain Curve ◽  
Alloy Sheet ◽  
Aluminum Alloy Sheet ◽  
Stress Strain Curve ◽  
Stress Strain

The Research of Aluminum Alloy Honeycomb Sandwich Panel Mechanical Properties in Out-Plane Compression Test

2012 ◽  
Vol 450-451 ◽  
pp. 252-256
Author(s):  
Jin Ping Hu
Keyword(s):  
Aluminum Alloy ◽  
Sandwich Panel ◽  
Split Hopkinson Pressure Bar ◽  
Deformation Behaviour ◽  
Strain Curve ◽  
Average Stress ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Honeycomb Sandwich ◽  
Aluminum Alloy Honeycomb

This paper first studied aluminum alloy honeycomb sandwich panel in out- plane static compress test.Through analyzing deformation characteristics, the loads-displacement relationship was obtained and are described by the average stress-strain curve. Secondly, using the Split Hopkinson Pressure Bar device of impact test, deformation behaviour，dynamic average stress-strain curve data and so on were got under different loading rates, thus learned impact dynamics characteristics of that.

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.

Stress-strain curve of paper revisited

2012 ◽  
Vol 27 (2) ◽  
pp. 318-328 ◽  
Author(s):  
Svetlana Borodulina ◽  
Artem Kulachenko ◽  
Mikael Nygårds ◽  
Sylvain Galland
Keyword(s):  
Three Dimensional ◽  
Strain Curve ◽  
Failure Behavior ◽  
Three Dimensional Model ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Fiber Network ◽  
Bonding Parameters ◽  
Particle Level ◽  
The Impact

Abstract We have investigated a relation between micromechanical processes and the stress-strain curve of a dry fiber network during tensile loading. By using a detailed particle-level simulation tool we investigate, among other things, the impact of “non-traditional” bonding parameters, such as compliance of bonding regions, work of separation and the actual number of effective bonds. This is probably the first three-dimensional model which is capable of simulating the fracture process of paper accounting for nonlinearities at the fiber level and bond failures. The failure behavior of the network considered in the study could be changed significantly by relatively small changes in bond strength, as compared to the scatter in bonding data found in the literature. We have identified that compliance of the bonding regions has a significant impact on network strength. By comparing networks with weak and strong bonds, we concluded that large local strains are the precursors of bond failures and not the other way around.

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