Experimental study and computer simulation of changes in the residual stresses of structure defects in shape memory alloys

2000 ◽  
Author(s):  
Teodor M. Breczko ◽  
Krzysztof Kus
Keyword(s):  
Experimental Study ◽  
Computer Simulation ◽  
Residual Stresses ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Structure Defects

Experimental Study of Tungsten Inert Gas Pulsed Welding Applied to Ni-Ti Shape Memory Alloy Wires

2015 ◽  
Vol 1765 ◽  
pp. 153-158 ◽  
Author(s):  
Luiz F.A. Rodrigues ◽  
Fernando A. Amorim ◽  
Francisco F.R. Pereira ◽  
Carlos J. de Araújo
Keyword(s):  
Experimental Study ◽  
Shape Memory Alloy ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Welding Process ◽  
Functional Materials ◽  
Inert Gas ◽  
Scanning Calorimetry ◽  
Dissimilar Joints ◽  
Mechanical Cycling

ABSTRACTShape memory alloys are functional materials that can recover plastic strains between 2 and 6%. This property can be used to produce actuators for many areas as medicine, robotic, aeronautic and others. Recently, it has been observed the particular interest for shape memory alloys welding, especially to obtain Ni-Ti similar and dissimilar joints and fabricate simple or complex structures. In this sense, this work present an experimental study of tungsten inert gas pulsed welding applied to Ni-Ti shape memory alloy wires with 0.9 mm in diameter, previously heat treated at 450 °C for 20 minutes and air cooled. For that, it was carried out tensile tests at isothermal temperatures from 40 °C to 90 °C (steps of 10 °C) for welded and unwelded wires. The transformation temperatures obtained from differential scanning calorimetry were compared to verify the effect of welding process. It was also performed a stabilization process by mechanical cycling in some welded and unwelded Ni-Ti wires. The results showed a low strength and strain capacity of the weld joint at higher temperatures. Although, at lowest temperature, close to 40 °C, it was observed higher values of maximum stress and strain for welded Ni-Ti wires.

scholarly journals Bayesian Calibration and Uncertainty Quantification for a Physics-Based Precipitation Model of Nickel–Titanium Shape-Memory Alloys

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Gustavo Tapia ◽  
Luke Johnson ◽  
Brian Franco ◽  
Kubra Karayagiz ◽  
Ji Ma ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Uncertainty Quantification ◽  
Simulation Models ◽  
Research Area ◽  
Nickel Titanium ◽  
Bayesian Calibration ◽  
Materials Simulation ◽  
Computer Simulation Models

Uncertainty quantification (UQ) is an emerging field that focuses on characterizing, quantifying, and potentially reducing, the uncertainties associated with computer simulation models used in a wide range of applications. Although it has been successfully applied to computer simulation models in areas such as structural engineering, climate forecasting, and medical sciences, this powerful research area is still lagging behind in materials simulation models. These are broadly defined as physics-based predictive models developed to predict material behavior, i.e., processing-microstructure-property relations and have recently received considerable interest with the advent of emerging concepts such as Integrated Computational Materials Engineering (ICME). The need of effective tools for quantifying the uncertainties associated with materials simulation models has been identified as a high priority research area in most recent roadmapping efforts in the field. In this paper, we present one of the first efforts in conducting systematic UQ of a physics-based materials simulation model used for predicting the evolution of precipitates in advanced nickel–titanium shape-memory alloys (SMAs) subject to heat treatment. Specifically, a Bayesian calibration approach is used to conduct calibration of the precipitation model using a synthesis of experimental and computer simulation data. We focus on constructing a Gaussian process-based surrogate modeling approach for achieving this task, and then benchmark the predictive accuracy of the calibrated model with that of the model calibrated using traditional Markov chain Monte Carlo (MCMC) methods.

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