Constitutive models to predict flow stress in Austenitic Stainless Steel 316 at elevated temperatures
Strain, strain rate and temperature have a significant impact on the flow stress of a material.
To study the impact of these factors on flow stress, quite a few empirical, semi-empirical …
To study the impact of these factors on flow stress, quite a few empirical, semi-empirical …
Prediction of flow stress in Ti–6Al–4V alloy with an equiaxed α+ β microstructure by artificial neural networks
Flow stress during hot deformation depends mainly on the strain, strain rate and
temperature, and shows a complex and nonlinear relationship with them. A number of semi …
temperature, and shows a complex and nonlinear relationship with them. A number of semi …
Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network
Flow stress during hot deformation depends mainly on the strain, strain rate and
temperature, and shows a complex nonlinear relationship with them. A number of semi …
temperature, and shows a complex nonlinear relationship with them. A number of semi …
Hot compressive deformation behaviour and constitutive equations of Mg–Pb–Al–1B–0.4 Sc alloy
Y Sun, L Bao, Y Duan - Philosophical Magazine, 2021 - Taylor & Francis
The hot compression features and microstructural evolution of Mg–Pb–Al–1B–0.4 Sc alloy
deformed at the strain rate range of 0.001–1 s− 1 and the temperature range of 493–613 K …
deformed at the strain rate range of 0.001–1 s− 1 and the temperature range of 493–613 K …
[HTML][HTML] Machine learning approach for prediction of hydrogen environment embrittlement in austenitic steels
This study introduces a machine learning approach to predict the effect of alloying elements
and test conditions on the hydrogen environment embrittlement (HEE) index of austenitic …
and test conditions on the hydrogen environment embrittlement (HEE) index of austenitic …
Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys
An artificial neural network model was developed to correlate the relationship between the
alloying elements (Al, V, Fe, O, and N) and heat treatment temperature (inputs) with the …
alloying elements (Al, V, Fe, O, and N) and heat treatment temperature (inputs) with the …
Modeling the hot deformation behaviors of as-extruded 7075 aluminum alloy by an artificial neural network with back-propagation algorithm
G Quan, Z Zou, T Wang, B Liu, J Li - High Temperature Materials and …, 2017 - degruyter.com
In order to investigate the hot deformation behaviors of as-extruded 7075 aluminum alloy,
the isothermal compressive tests were conducted at the temperatures of 573, 623, 673 and …
the isothermal compressive tests were conducted at the temperatures of 573, 623, 673 and …
Prediction of mechanical properties of ASS 304 in superplastic region using artificial neural networks
Flow stress during hot deformation depends mainly on the strain, strain rate and
temperature, and shows an intricate relationship with them. In this paper an artificial neural …
temperature, and shows an intricate relationship with them. In this paper an artificial neural …
Modeling constituent–property relationship of polyvinylchloride composites by neural networks
BRS Reddy, M Premasudha, BB Panigrahi… - Polymer …, 2020 - Wiley Online Library
The purpose of this study is to develop an artificial neural network (ANN) model to predict
and analyze the relationship between properties and process parameters of polyvinyl …
and analyze the relationship between properties and process parameters of polyvinyl …
High temperature formability prediction of dual phase brass using phenomenological and physical constitutive models
Characterizing the high temperature flow behavior of a lead bearing duplex brass in a wide
range of forming temperatures (673-1073 K) and strain rates (0.001-0.1 s− 1) has been …
range of forming temperatures (673-1073 K) and strain rates (0.001-0.1 s− 1) has been …