Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Constitutive Analysis on High-Temperature Flow Behavior of 3Cr-1Si-1Ni Ultra-High Strength Steel for Modeling of Flow Stress

(2019) Bingwang Lei et al.   Metals

Strain Compensation Constitutive Model and Parameter Optimization for Nb-Contained 316LN

(2019) Jingdan Li et al.   Metals

Hot Deformation Behavior of a 2024 Aluminum Alloy Sheet and its Modeling by Fields-Backofen Model Considering Strain Rate Evolution

(2019) Zhubin He et al.   Metals

Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1

(2019) Johan Stendal et al.   Metals

A Comparative Study on Arrhenius and Johnson–Cook Constitutive Models for High-Temperature Deformation of Ti2AlNb-Based Alloys

(2019) Zhubin He et al.   Metals

Johnson Cook Material and Failure Model Parameters Estimation of AISI-1045 Medium Carbon Steel for Metal Forming Applications

(2019) Mohanraj Murugesan et al.   Materials

A Modified Back Propagation Artificial Neural Network Model Based on Genetic Algorithm to Predict the Flow Behavior of 5754 Aluminum Alloy

(2018) Changqing Huang et al.   Materials

A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy

(2016) H.R. Rezaei Ashtiani et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Modeling of flow behavior for 7050-T7451 aluminum alloy considering microstructural evolution over a wide range of strain rates

(2016) Guang Chen et al.   MECHANICS OF MATERIALS

Artificial neural networks for mechanical strength prediction of lightweight mortar

(2016) S V Razavi et al.   Scientific Research and Essays

Constitutive Relationship Modeling and Characterization of Flow Behavior under Hot Working for Fe–Cr–Ni–W–Cu–Co Super-Austenitic Stainless Steel

(2015) Li-Chih Yang et al.   Metals

Artificial Neural Network Modeling of Flow Stress in Hot Rolling

(2014) Parya Aghasafari et al.   ISIJ INTERNATIONAL

Constitutive modelling of the flow behaviour of a β titanium alloy at high strain rates and elevated temperatures using the Johnson–Cook and modified Zerilli–Armstrong models

(2014) Hongyi Zhan et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Constitutive flow stress formulation, model validation and FE cutting simulation for AA7075-T6 aluminum alloy

(2014) Uma Maheshwera Reddy Paturi et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti–6Al–2Zr–1Mo–1V alloy by artificial neural network

(2013) Guo-zheng Quan et al.   MATERIALS & DESIGN

A comparative study on Johnson–Cook, modified Johnson–Cook and Arrhenius-type constitutive models to predict the high temperature flow stress in 20CrMo alloy steel

(2013) An He et al.   MATERIALS & DESIGN

Comparative study on constitutive relationship of as-cast Ti60 titanium alloy during hot deformation based on Arrhenius-type and artificial neural network models

(2013) Wenwen Peng et al.   MATERIALS & DESIGN

A comparative study on modified Johnson Cook, modified Zerilli–Armstrong and Arrhenius-type constitutive models to predict the hot deformation behavior in 28CrMnMoV steel

(2013) Hong-Ying Li et al.   MATERIALS & DESIGN

A modified Johnson Cook model for elevated temperature flow behavior of T24 steel

(2013) Hong–Ying Li et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Constitutive relationships of hot stamping boron steel B1500HS based on the modified Arrhenius and Johnson–Cook model

(2013) Huiping Li et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Constitutive relationship model of TC21 alloy based on artificial neural network

(2013) La-feng GUO et al.   TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA

Application of constitutive and neural network models for prediction of high temperature flow behavior of Al/Mg based nanocomposite

(2013) V. SENTHILKUMAR et al.   TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA

A comparative study on Arrhenius-type constitutive equations and artificial neural network model to predict high-temperature deformation behaviour in 12Cr3WV steel

(2012) X. Xiao et al.   COMPUTATIONAL MATERIALS SCIENCE

A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models

(2012) Ying Han et al.   COMPUTATIONAL MATERIALS SCIENCE

Modeling of strain hardening and dynamic recrystallization of ZK60 magnesium alloy during hot deformation

(2012) Yun-bin HE et al.   TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA

Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy

(2011) Yanchun Zhu et al.   COMPUTATIONAL MATERIALS SCIENCE

Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel

(2011) Hong-Ying Li et al.   MATERIALS & DESIGN

A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel

(2011) Guoliang Ji et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

A critical review of experimental results and constitutive descriptions for metals and alloys in hot working

(2010) Y.C. Lin et al.   MATERIALS & DESIGN

A comparative study on Johnson Cook, modified Zerilli–Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr–1Mo steel

(2009) Dipti Samantaray et al.   COMPUTATIONAL MATERIALS SCIENCE