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Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance (CROSBI ID 294235)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Smokvina Hanza, Sunčana ; Marohnić, Tea ; Iljkić, Dario ; Basan, Robert Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance // Metals, 11 (2021), 5; 714, 14. doi: 10.3390/met11050714

Podaci o odgovornosti

Smokvina Hanza, Sunčana ; Marohnić, Tea ; Iljkić, Dario ; Basan, Robert

engleski

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 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.

low-alloy steels ; quenching ; mechanical properties ; hardness ; artificial neural networks

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Podaci o izdanju

11 (5)

2021.

714

14

objavljeno

2075-4701

10.3390/met11050714

Povezanost rada

Metalurgija, Strojarstvo, Temeljne tehničke znanosti

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