Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance

Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy st...

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Bibliographic Details
Published in:Metals (Basel ) 2021-05, Vol.11 (5), p.714
Main Authors: Smokvina Hanza, Sunčana, Marohnić, Tea, Iljkić, Dario, Basan, Robert
Format: Article
Language:English
Subjects:
Alloying elements
Artificial neural networks
Austenitizing
Bainitic transformations
Carbon
Chemical composition
Chromium
Configurations
Cooling
Grain size
Hardness
Low alloy steels
Manganese
Materials engineering
Materials substitution
Mechanical properties
Molybdenum
Neural networks
Nickel
Precipitation hardening
Process controls
quenching
Silicon
Steel
Variables
Yield stress
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Summary:Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based on chemical composition, by introducing the specific Jominy distance as a new input variable. For prediction of total hardness after continuous cooling of steel (output variable), ANNs were developed for different combinations of inputs. Input variables for the first configuration of ANNs were the main alloying elements (C, Si, Mn, Cr, Mo, Ni), the austenitizing temperature, the austenitizing time, and the cooling time to 500 °C, while in the second configuration alloying elements were substituted by the specific Jominy distance. Comparing the results of total hardness prediction, it can be seen that the ANN using the specific Jominy distance as input variable (runseen = 0.873, RMSEunseen = 67, MAPE = 14.8%) is almost as successful as ANN using main alloying elements (runseen = 0.940, RMSEunseen = 46, MAPE = 10.7%). The research results indicate that the prediction of total hardness of steel can be successfully performed only based on four input variables: the austenitizing temperature, the austenitizing time, the cooling time to 500 °C, and the specific Jominy distance.
ISSN:2075-4701
2075-4701
DOI:10.3390/met11050714