The influence of the chemical composition and the grain size of retained austenite in an ultra-fine grained medium manganese steel on the martensite transformation kinetics was investigated by means of dilatometry. A modified Koistinen–Marburger type exponential function was developed for the martensite transformation kinetics, which includes the influence of the size and the composition of the austenite. Whereas the ultra-fine grained retained austenite had a remarkably low M _s temperature, the kinetics of its transformation to martensite was accelerated.

The tempering of low-alloy steels is important for controlling the mechanical properties required for industrial fields. Several studies have investigated the relationships between the input and target values of materials using machine learning algorithms. The limitation of machine learning algorithms is that the mechanism of how the input values affect the output has yet to be confirmed despite numerous case studies. To address this issue, we trained four machine learning algorithms to control the hardness of low-alloy steels under various tempering conditions. The models were trained using the tempering temperature, holding time, and composition of the alloy as the inputs. The input data were drawn from a database of more than 1900 experimental datasets for low-alloy steels created from the relevant literature. We selected the random forest regression (RFR) model to analyze its mechanism and the importance of the input values using Shapley additive explanations (SHAP). The prediction accuracy of the RFR for the tempered martensite hardness was better than that of the empirical equation. The tempering temperature is the most important feature for controlling the hardness, followed by the C content, the holding time, and the Cr, Si, Mn, Mo, and Ni contents.

Fe-based bulk metallic glasses (BMGs) are a unique class of materials that are attracting attention in a wide variety of applications owing to their physical properties. Several studies have investigated and designed the relationships between alloy composition and thermal properties of BMGs using an artificial neural network (ANN). The limitation of the wide-scale use of these models is that the required composition is yet to be found despite numerous case studies. To address this issue, we trained an ANN to design Fe-based BMGs that predict the thermal properties. Models were trained using only the composition of the alloy as input and were created from a database of more than 150 experimental data of Fe-based BMGs from relevant literature. We adopted these ANN models to design BMGs with thermal properties to satisfy the intended purpose using particle swarm optimization. A melt spinner was employed to fabricate the designed alloys. X-ray diffraction and differential thermal analysis tests were used to evaluate the specimens.

In this study, the average grain size was evaluated from a microstructure image using a convolutional neural network. Since the grain size in a microstructure image can be directly measured and verified in the original image, unlike the chemical composition or mechanical properties of material, it is more appropriate to validate the training results quantitatively. An analysis of microstructure images, such as grain size, can be performed manually or using image analysis software; however, it is expected that the analysis would be simpler and faster with machine learning. Microstructure images were created using a phase-field simulation, and machine learning was carried out with a convolutional neural network model. The relationship between the microstructure image and the average grain size was not judged by classification, as the goal was to have different results for each microstructure using regression. The results showed high accuracy within the training range. The average grain sizes of experimental images with explicit grain boundary were well estimated by the network. The mid-layer image was analyzed to examine how the network understood the input microstructure image. The network seemed to recognize the curvatures of the grain boundaries and estimate the average grain size from these curvatures.