Deep Learning to Predict Deterioration Region of Hot Ductility in High-Mn Steel by Using the Relationship between RA Behavior and Time-Temperature-Precipitation

Abstract

Reduction of area (RA) measurement in a hot ductility test is widely used to define the susceptibility of surface crack of cast steel, but the test is complex because it entails processes such as specimen fabrication, heat treatment, tensile testing, and analysis. As an alternative, this study proposes a model that can predict RA. The model exploits the relationship between precipitation and RA behavior, which has a major effect on hot ductility degradation in high-Mn steels. Hot ductility tests were performed using four grades of high-Mn steels that had different V-Mo compositions, and the RA behavior was compared with the precipitation behavior obtained from a time-temperature-precipitation (TTP) graph. The ductility deterioration of high-Mn steels shows a tendency to start at the nose temperature TN at which precipitation is most severe. Using this relationship, we developed a model to predict the hot ductility degradation temperature of high-Mn steels. TN was calculated using J-matpro software (version 12) for 1500 compositions of high-Mn steels containing the precipitating elements V, Mo, Nb, and Ti, and by applying this to a deep neural network (DNN), then using the result to develop a model that can predict TN for various compositions of high-Mn steel. The model was tested by comparing its predicted RA degradation temperature with RAs extracted from reference data for five high-Mn steels. In all five steels, the temperature at which the RA decreases coincided with the value predicted by the DNN model. Use of this model can eliminate the cost and time required for hot ductility testing to measure RA.

Keywords

• reduction in area
• deep neural network
• machine learning
• time-temperature-precipitation (TTP) graph
• crack
• continuous casting