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

Abstract

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.

Machine Learning-based Investigation of the Influencing Factors on the Hardness of Steel Materials

“…This study investigated two different group parameters (defined as Configurations I and II) among six chemical components, three process parameters, and specific Jominy distance, which is the same as parameter sets in Sunˇcana et al's work [13] . Table 2 displays the input variables used for the two configurations.…”

“…However, these require inputting a large number of variables and may not accurately specify the content of the chemical composition. Therefore, this paper chose the specific Jominy distance proposed by Sunčana et al [13] as one of the input variables, establishing Configuration 2. The two-stage clustering method was used for data classification preprocessing, focusing on comparing the fitting effects of the two configurations.…”

“…This study utilizes a total of 423 experimental data samples from Sunˇcana et al's work [13] and, its data visualization reveals an abnormal relationship between the variable t500 and hardness. Figure 1 data for one type of steel (15CrNi6), with the variable t500 representing the time required for steel to cool down to 500°C, indicative of the cooling rate.…”

“…It is worth noting that the experimental data obtained from previous works may vary due to a lack of detailed processing parameters in modern steel material manufacturing. Sunˇcana et al [13] , have sought to improve the accuracy of steel hardness predictions using ANNs by incorporating a new input variable -Jominy distance. Their study demonstrated that accurate predictions of steel hardness could be made using four variables: austenite temperature, austenite time, cooling time to 500 degrees, and Jominy distance.…”

“…Additionally, two configurations of input variables are adopted for each data set. Configuration I's input variables include element composition and processes, while Configuration II introduces the Jominy distance proposed by Sunˇcana et al [13] , replacing the element composition in Configuration I. In Section 3, after briefly introducing ANN model construction and GRNN, we present an improved GRNN to study the two types of data and in Section 4, make a comparison between Configuration I and Configuration II.…”