Modeling the yield strength of hot strip low carbon steels by artificial neural network

2009 ◽  
Vol 30 (9) ◽  
pp. 3653-3658 ◽  
Author(s):  
M. Botlani Esfahani ◽  
M.R. Toroghinejad ◽  
A.R. Key Yeganeh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Yield Strength ◽  
Carbon Steels ◽  
Low Carbon ◽  
Low Carbon Steels ◽  
Hot Strip ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Network to the Prediction of Tensile Properties in High-Strength Low-Carbon Bainitic Steels

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1314
Author(s):  
Sang-In Lee ◽  
Seung-Hyeok Shin ◽  
Byoung-Chul Hwang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Yield Strength ◽  
Tensile Properties ◽  
Uniform Elongation ◽  
High Strength ◽  
Low Carbon ◽  
Ann Model ◽  
Bainitic Steels ◽  
Artificial Neural

An artificial neural network (ANN) model was designed to predict the tensile properties in high-strength, low-carbon bainitic steels with a focus on the fraction of constituents such as PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), and BF (bainitic ferrite). The input parameters of the model were the fraction of constituents, while the output parameters of the model were composed of the yield strength, yield-to-tensile ratio, and uniform elongation. The ANN model to predict the tensile properties exhibited a higher accuracy than the multi linear regression (MLR) model. According to the average index of the relative importance for the input parameters, the yield strength, yield-to-tensile ratio, and uniform elongation could be effectively improved by increasing the fraction of AF, bainitic microstructures (AF, GB, and BF), and PF, respectively, in terms of the work hardening and dislocation slip behavior depending on their microstructural characteristics such as grain size and dislocation density. The ANN model is expected to provide a clearer understanding of the complex relationships between constituent fraction and tensile properties in high-strength, low-carbon bainitic steels.

scholarly journals Change in Yield Strength of Ultra Low Carbon Steels with Cr Addition

2010 ◽  
Vol 96 (4) ◽  
pp. 162-171 ◽  
Author(s):  
Yoshimasa Funakawa ◽  
Takumi Ujiro
Keyword(s):  
Yield Strength ◽  
Carbon Steels ◽  
Low Carbon ◽  
Low Carbon Steels ◽  
Cr Addition

MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL

2013 ◽  
Vol 02 (01) ◽  
pp. 1350004 ◽  
Author(s):  
S. RATH ◽  
P. P. SENGUPTA ◽  
A. P. SINGH ◽  
A. K. MARIK ◽  
P. TALUKDAR
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Mathematical Models ◽  
Hybrid Model ◽  
Data Driven ◽  
Roll Force ◽  
Hot Strip ◽  
Artificial Neural

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.

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