Metastable β-Ti alloys have become one of the most attractive implant materials due to their high biocorrosion resistance, biocompatibility, and mechanical properties, including lower Young’s modulus values. Mechanical properties of these alloys are strongly dependent on the final microstructure, which is controlled by thermomechanical treatment processing, in particular the Young’s modulus and hardness. The aim of this work was to analyze the influence of phase precipitations in heat-treated Ti-12Mo-xNb (x = 0, 3, 8, 13, 17, and 20) alloys. The alloys were prepared via arc melting and treated at 950 °C/1 h, and then quenched in water. The microstructures were analyzed by optical microscopy, transmission electron microscopy, and X-ray diffraction. Mechanical properties were based on Vickers microhardness tests and Young’s modulus measurements. Microstructural characterization showed that α″ and ω stability is a function of Nb content for the Ti–12Mo base alloy. Nb addition resulted in the suppression of the α″ phase and decrease in the ω phase volume fraction. Although the ω phase decreased with higher Nb contents, ω particles with ellipsoidal morphology were still observed in the Ti–12Mo–20Nb alloy. The α″ phase suppression by Nb addition caused a marked increase in the Young’s modulus, which decreased back to lower values with higher Nb concentrations. On other hand, the decrease in the ω phase continuously reduced alloy hardness. The study of the effect of chemical composition in controlling the volume fraction of these phases is an important step for the development of β-Ti alloys with functional properties.

Duplex stainless steels (commonly known as DSS) are a class of stainless steels with a microstructure formed by two main phases: ferrite and austenite. They are used in a wide range of applications, such as chemical processing, in maritime environments and in the oil and gas industries. In most cases, DSS are chosen based on their strength and corrosion resistance for various environments. When exposed to temperatures above 600°C though, the balance of alloying elements can be modified due to precipitation of various secondary phases, such as sigma (σ) and chi (χ). The sigma phase is typically enriched with Cr and Mo, so its formation can lead to a drastic deterioration in toughness, corrosion resistance, and weldability of duplex stainless steels. To prevent damages to these steels due to the formation of sigma phase, the understanding of such transformation becomes mandatory, not only during the development of these steels but also during their processing. In this research, samples from a lean duplex steel UNS S32304 are subjected to a temperature of 800°C and analyzed in situ by X-ray diffraction. Thus, the kinetics of phase transformations occurring in duplex stainless steels are observed in real time.

X-ray diffraction has been applied in the investigation of phase formation in steels, operating in industrial environments. In this work, identification and quantification of phases by X-ray diffraction and peak fitting, using the Rietveld method, were employed. In a first scenario, two different types of steels, subjected to abrasive surface cleaning, suffered contamination from the blasting operation that compromise between 10 and 36% of the blasted surface, as revealed by the quantitative phase analysis. Such high values of particulates can jeopardize the corrosion protection offered by posterior coating application. In a second scenario, duplex steels (DS) subjected to aggressive environments and high temperatures of service went through phase transformation that formed amounts up to 3.5% of a deleterious phase, known as sigma phase. This phase compromises the steel mechanical resistance and corrosion protection, and its quantification is crucial for the assurance of the material integrity. The quantitative phase analysis (QPA) by X-ray diffraction provided the diagnosis of forthcoming problems related to the presence of such phases in the investigated steels, allowing the optimization of techniques and the choice of correct process parameters.

Artificial Intelligence [AI] has been of great discussion lately and one can perceive its use in many aspects of modern life. In science, and more specifically in Materials Sciences, AI has been employed for many different applications. Machine Learning (ML) has been historically linked to Artificial Intelligence (AI) for many decades. Some basic concepts of ML can be traced from the 1930s, but it was only during the 1980s and 1990s that ML really started to be used in a stronger and well-organized fashion, due to the development of more efficient algorithms from better and more robust data processing machines. This chapter presents a review on the recent works of distinct research groups that have been using Machine Learning [ML], which is one of many different methods of AI, as a tool for predicting steel properties. A brief definition of ML is given at the beginning of the chapter, followed by some of the most relevant examples of ML use to exemplify the power of this AI method for the development of steel engineering.