Morphology and Crystallography Analyses of HSLA Steels with Hardenability Enhanced by Tailored C–Ni Collocation

High hardenability is of great importance to ultra-heavy steel plates and can be achieved by tailoring the composition of steel. In this study, the continuous cooling transformation (CCT) curves of two high-strength low-alloy (HSLA) steels (0.16C-0.92Ni steel and 0.12C-1.86Ni steel) were elucidated to reveal the significance of C–Ni collocation on hardenability from the perspective of morphology and crystallography. At a low cooling rate (0.5 °C/s), the 0.12C-1.86Ni steel showed higher microhardness than 0.16C-0.92Ni steel. The microstructure in 0.16C-0.92Ni steel was mainly granular bainite with block-shaped martensite/austenite islands (M/A islands), while that in 0.12C-1.86Ni steel was typically lath bainite with film-shaped M/A islands, denoting that the 0.12C-1.86Ni steel is of higher hardenability. Moreover, the 0.12C-1.86Ni steel exhibited a higher density of block boundaries, especially V1/V2 boundaries. The higher density of block boundaries resulted from the weakened variant selection due to the larger transformation driving force and more self-accommodation of transformation strain induced by the reduced carbon and increased nickel content.

Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting

Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.

Texture Memory in Hexagonal Metals and Its Mechanism

Texture memory is a phenomenon in which retention of initial textures occurs after a complete cycle of forward and backward transformations, and it occurs in various phase-transforming materials including cubic and hexagonal metals such as steels and Ti and Zr alloys. Texture memory is known to be caused by the phenomena called variant selection, in which some of the allowed child orientations in an orientation relationship between the parent and child phases are preferentially selected. Without such variant selection, the phase transformations would randomize preferred orientations. In this article, the methods of prediction of texture memory and mechanisms of variant selections in hexagonal metals are explored. The prediction method using harmonic expansion of orientation distribution functions with the variant selection in which the Burgers orientation relationship, {110}β//{0001}α-hex <11¯1>β//21¯1¯0α-hex, is held with two or more adjacent parent grains at the same time, called “double Burgers orientation relation (DBOR)”, is introduced. This method is shown to be a powerful tool by which to analyze texture memory and ultimately provide predictive capabilities for texture changes during phase transformations. Variation in nucleation and growth rates on special boundaries and an extensive growth of selected variants are also described. Analysis of textures of commercially pure Ti observed in situ by pulsed neutron diffraction reveals that the texture memory in CP-Ti is indeed quite well predicted by consideration of the mechanism of DBOR. The analysis also suggests that the nucleation and growth rates on the special boundary of 90° rotation about 21¯1¯0α-hex should be about three times larger than those of the other special boundaries, and the selected variants should grow extensively into not only one parent grain but also other grains in α-hex(hexagonal)→β(bcc) transformation. The model calculations of texture development during two consecutive cycles of α-hex→β→α-hex transformation in CP-Ti and Zr are also shown.