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

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

Download Full-text

Dynamic behavior and modified artificial neural network model for predicting flow stress during hot deformation of Alloy 925

Materials Today Communications ◽

10.1016/j.mtcomm.2020.101329 ◽

2020 ◽

Vol 25 ◽

pp. 101329 ◽

Cited By ~ 1

Author(s):

Yulong Zhu ◽

Yu Cao ◽

Cunjian Liu ◽

Rui Luo ◽

Na Li ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flow Stress ◽

Dynamic Behavior ◽

Network Model ◽

Neural Network Model ◽

Hot Deformation ◽

Artificial Neural Network Model ◽

Artificial Neural

Download Full-text

Artificial Neural Network Modeling of Flow Stress in Hot Rolling

ISIJ International ◽

10.2355/isijinternational.54.872 ◽

2014 ◽

Vol 54 (4) ◽

pp. 872-879 ◽

Cited By ~ 4

Author(s):

Parya Aghasafari ◽

Hamid Abdi ◽

Mahmoud Salimi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flow Stress ◽

Hot Rolling ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Artificial Neural

Download Full-text

Artificial Neural Network for Predicting the Flow Behaviors of Hot Compressed 2124-T851 Aluminum Alloy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.146-147.720 ◽

2010 ◽

Vol 146-147 ◽

pp. 720-723

Author(s):

Yong Cheng Lin ◽

Xiao Min Chen ◽

Yu Chi Xia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Aluminum Alloy ◽

Flow Stress ◽

Strain Rate ◽

Learning Algorithm ◽

Ann Model ◽

Wide Range ◽

Feed Forward Network ◽

Artificial Neural

The compressive deformation experiments of 2124-T851 aluminum alloy were carried out over a wide range of temperature and strain rate. An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the flow behaviors of hot compressed 2124-T851 aluminum alloy and working conditions. The input parameters of the model consist of strain rate, forming temperature and deformation degree whereas flow stress is the output. A three layer feed-forward network with 15 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. Good performance of the ANN model is achieved. The predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.

Download Full-text