The effect of the sulfur content on the inclusion and microstructure characteristics of steels where Ti2O3 and TiO2 have been added was studied. Based on the microscopic examinations, it is found in the steel samples with Ti2O3 additions that the area fraction of intragranular ferrite decreases from 52.68% to 39.09% as the sulfur content increases from 0.009 mass.% to 0.030 mass.%. In the steel samples with TiO2 additions, this value also decreases from 49.05% to 36.26% as the sulfur content increases. The nucleant inclusion was identified as a TiOx+MnS phase based on SEM-EDS measurements as well as on equilibrium calculations with thermodynamic calculation software, Thermo-Calc. Also, TiOx was found to be the nucleation site for an intragranular ferrite formation. Moreover, the nucleation probability increases with an increased inclusion size. It is also noted that the nucleation probability decreases slightly with an increased sulfur content. The minimum size of TiOx+MnS inclusions for an IGF nucleation is about 0.85 μm in the present samples. Furthermore, this minimum size of TiOx inclusions is shifted to a size of about 0.5 μm by excluding the depth of a MnS layer. In addition, the effective nucleation size range of TiOx inclusions in the steels, where Ti2O3 and TiO2 had been added, is smaller than that of TiN+Mn-Al-Si-Ti-O inclusions in steel samples where TiN had been added.
The martensite start temperature (M s) is a critical parameter when designing high-performance steels and their heat treatments. It has, therefore, attracted significant interest over the years. Numerous methodologies, such as thermodynamics-based, linear regression and artificial neural network (ANN) modeling, have been applied. The application of data-driven approaches, such as ANN modeling, or the wider concept of machine learning (ML), have shown limited technical applicability, but considering that these methods have made significant progress lately and that materials data are becoming more accessible, a new attempt at data-driven predictions of the M s is timely. We here investigate the usage of ML to predict the M s of steels based on their chemical composition. A database of the M s vs alloy composition containing 2277 unique entries is collected. It is ensured that all alloys are fully austenitic at the given austenitization temperature by thermodynamic calculations. The ML modeling is performed using four different ensemble methods and ANN. Train-test split series are used to evaluate the five models, and it is found that all four ensemble methods outperform the ANN on the current dataset. The reason is that the ensemble methods perform better for the rather small dataset used in the present work. Thereafter, a validation dataset of 115 M s entries is collected from a new reference and the final ML model is benchmarked vs a recent thermodynamics-based model from the literature. The ML model provides excellent predictions on the validation dataset with a root-mean-square error of 18, which is slightly better than the thermodynamics-based model. The results on the validation dataset indicate the technical usefulness of the ML model to predict the M s in steels for design and optimization of alloys and heat treatments. Furthermore, the agility of the ML model indicates its advantage over thermodynamics-based models for M s predictions in complex multicomponent steels.
The quantitative analysis of inclusion and microstructure characteristics in the steels with TiN additions has been studied. The typical inclusion was detected to be a TiN + Mn‐Al‐Si‐Ti‐O + MnS phase. This identification was based on the measurements of scanning electron microscopy with energy‐dispersive X‐ray spectrometer (SEM‐EDS), electron probe microanalysis (EPMA), which equipped wavelength‐dispersive X‐ray spectroscopy (WDS), and equilibrium calculations by using the commercial software Thermo‐Calc. TiN was found to be the effective nucleation site for the formation of intragranular ferrite (IGF). Furthermore, the increased inclusion size led to the increased probability of IGF nucleation. In addition, this probability of IGF nucleation was slightly decreased with the increased sulfur content. This tendency could fit the tendency of the area fraction of IGF in the steels containing different sulfur contents.
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