Next Article in Journal
NdFeB Magnets Recycling Process: An Alternative Method to Produce Mixed Rare Earth Oxide from Scrap NdFeB Magnets
Next Article in Special Issue
Modeling the Chemical Composition of Ferritic Stainless Steels with the Use of Artificial Neural Networks
Previous Article in Journal
Load Characteristics in Taylor Impact Test on Projectiles with Various Nose Shapes
Previous Article in Special Issue
Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization
Article

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

University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia
*
Author to whom correspondence should be addressed.
Academic Editors: Diego Celentano, Francesca Borgioli and Lijun Zhang
Metals 2021, 11(5), 714; https://doi.org/10.3390/met11050714
Received: 3 March 2021 / Revised: 2 April 2021 / Accepted: 20 April 2021 / Published: 27 April 2021
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. View Full-Text
Keywords: low-alloy steels; quenching; mechanical properties; hardness; artificial neural networks low-alloy steels; quenching; mechanical properties; hardness; artificial neural networks
Show Figures

Figure 1

MDPI and ACS Style

Smokvina Hanza, S.; Marohnić, T.; Iljkić, D.; Basan, R. Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance. Metals 2021, 11, 714. https://doi.org/10.3390/met11050714

AMA Style

Smokvina Hanza S, Marohnić T, Iljkić D, Basan R. Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance. Metals. 2021; 11(5):714. https://doi.org/10.3390/met11050714

Chicago/Turabian Style

Smokvina Hanza, Sunčana, Tea Marohnić, Dario Iljkić, and Robert Basan. 2021. "Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance" Metals 11, no. 5: 714. https://doi.org/10.3390/met11050714

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop