The presence of retained austenite (RA) has been proven beneficial for the formability of Advanced High Strength Steels. Therefore, multiple thermal austenite stabilization methods have been researched, such as Mn-partitioning during intercritical annealing in medium Mn steels. The Cyclic Partial Phase Transformation (CPPT) approach has been successful in obtaining localized Mn-enrichment at the ferrite/austenite interface through cyclic annealings at intercritical temperatures in a medium Mn steel. In this thesis, the possible application of CPPT heat treatments towards interfacial austenite stabilization at room temperature in medium Mn Dual Phase (DP) steels is studied. ThermoCalc modelling and dilatometry measurements were used to determine the optimal CPPT parameters for fully martensitic Fe-Mn-C-Si samples with varying concentrations of manganese. Scanning Electron Microscopy acquisitions of the resulting microstructure showed a strong influence from the initial microstructure. Samples which went through a full austenitization formed coarse martensitic islands and equiaxed ferrite grains. However, samples which were intercritically annealed from a fully martensitic state resulted in fine and elongated ferrite grains surrounded by martensite. X-Ray Diffraction analysis revealed that CPPT treated samples with 2wt.%Mn and 4wt.%Mn had a maximum RA volume fraction of 1wt.% and 4wt.% respectively. Up to 16wt.% of RA was obtained at room temperature through CPPT heat treatments for samples with 6wt.% Mn. This difference in results has been attributed to the concentration spike of Mn at the ferrite/austenite interface characterizing the NPLE austenite growth, as well as to the widening of the Mn-enriched zone through repeated isothermal intercritical annealing cycles. While a relatively high volume fraction of RA has been successfully obtained at room temperature in 6wt.% Mn DP steels, its interfacial morphology could not be confirmed due to the interference of the fine ferrite/martensite microstructure with the Electron Back-Scattered Diffraction analysis.

Bainite steels are in high demand in many application areas owing to their outstanding mechanical properties, mainly due to the presence of a combination of fine bainite plates and retained austenite. Understanding the complicated mechanism of bainite transformation is crucial to creating an optimum design. Researchers have addressed this challenge via computational modelling, where the transformation start temperatures of bainite (Bs) and martensite (Ms) are key indexes when designing highperformance steels and their heat treatments. This thesis aims to explore the potential that data collection has to create a model, using experimental results of the bainite transformation process. A list of metallurgical accepted claims is elaborated to assess the quality of the data. Principal component analysis and clustering techniques are used to identify patterns, most important features and main relationships in the dataset. The results confirm the exponential carbon dependence that bainite and martensite transformation temperatures have, therefore requiring nonlinear models to predict them. Following, regression models and machine learning algorithms based solely on the chemical composition are used to predict Bs and Ms. Train-test split series and cross-validation are used to evaluate the prediction and consistency of each model. The results show that the ensemble learning algorithms outperform the regression techniques. Random forest and gradient boosting decision tree provide excellent Ms prediction on the validation set with R2 values of 0.92 and 0.93. The smaller dataset size adds up to the complexity of bainite transformation, resulting in worse prediction models of Bs, where the random forest and gradient boosting decision tree R2 values are 0.68 and 0.67 respectively. Even though the models are showing signs of learning, the impact that outliers have demonstrates that the data is not good by itself to create a predictive model. The incorporation of microstructural and process parameters would provide significant advances to the models for designing bainitic steels.