Yunwei Gui

Bio: Yunwei Gui is an academic researcher from Henan University of Science and Technology. The author has contributed to research in topics: Nucleation. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

A combined machine learning and EBSD approach for the prediction of {10-12} twin nucleation in an Mg-RE alloy

Abstract: The room-temperature ductility of the Mg alloys is closely related to the deformation behavior of the twin. However, there are currently no effective criteria that can accurately predict in which grains twins will nucleate during plastic deformation. With the rapid development of artificial intelligence technology, the applications of machine learning in the microstructure design and predictions have become the highlight. In the present study, a novel Mg-4Y-3Nd-2Sm-0.5 Zr alloy is prepared so as to explore the twin nucleation behavior of Mg alloys by combining machine learning along with electron backscattered diffraction (EBSD) techniques. At a true strain of 0.05, twins are found in 68 grains of the 297 grains which are counted from the initial microstructure. Eight features that may affect the twin nucleation are selected, including the grain diameter, the number of neighboring grains, the Schmid factor, and so on. Furthermore, the relevant importance of eight features on twin nuclei are also sorted; the grain diameter of original grains and the Schmid factor of the tensile twins have the greatest influence on the twin nucleation. Three machine learning algorithms including XGBoost, ANN, and the proposed relevance based ensemble scheme are used to model the prediction of the twin nucleation. The proposed relevance based ensemble scheme achieved an AUC score of 0.880, which is higher than that of the ANN (0.879) and XGBoost (0.756). A better ROC and PR curve also validate the feasibility of the proposed scheme.

The role of {10–12} tensile twinning in plastic deformation and fracture prevention of magnesium alloys

Abstract: To improve the formability of magnesium alloy sheets, {10–12} tensile twins can be used to improve the plastic deformation ability of magnesium alloys at room temperature. In this paper, through the quasi-in-situ study of the AZ31B magnesium alloy samples prepared with {10–12} tensile twins during the uniaxial tensile process, the mechanism of tensile twinning to improve the plastic forming of magnesium alloys is further explored. The results show that the variant selection behavior of the tensile twin usually exhibits Schmid behavior, but sometimes non Schmid behavior, which leads to the change of the matrix grain orientation and the activation of the pyramidal II < c+a > slip more easily. Strain compatibility is the main factor affecting the variant selection behavior of transgranular twins, while the activation of the pyramidal II < c+a > slip of the matrix is almost geometric compatibility. The pyramidal II < c+a > slip is easily activated inside the twin, and its activation is determined by the strain compatibility between adjacent grains. Microcracks propagate along the grain boundaries with poor strain compatibility. Tensile twinning can coordinate the slip between adjacent grains and the strain transfer between twins, improve the strain compatibility between adjacent grains, and reduce microcrack propagation.

Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

Abstract: With its excellent property combinations and ability to specifically adjust tailor-made microstructures, steel is still the world’s most important engineering and construction material. To fulfill ever-increasing demands and tighter tolerances in today’s steel industry, steel research remains indispensable. The continuous material development leads to more and more complex microstructures, which is especially true for steel designs that include bainitic structures. This poses new challenges for the classification and quantification of these microstructures. Machine learning (ML) based microstructure classification offers exciting potentials in this context. This paper is concerned with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels by using machine learning techniques. For successful applications of ML-based classifications, a holistic approach combining computer science expertise and material science domain knowledge is necessary. Seven microstructure classes are considered: pearlite, martensite, and the bainitic subclasses degenerate pearlite, debris of cementite, incomplete transformation product, and upper and lower bainite, which can all be present simultaneously in one micrograph. Based on SEM images, textural features (Haralick parameters and local binary pattern) and morphological parameters are calculated and classified with a support vector machine. Of all second phase objects, 82.9% are classified correctly. Regarding the total area of these objects, 89.2% are classified correctly. The reported classification can be the basis for an improved, sophisticated microstructure quantification, enabling process–microstructure–property correlations to be established and thereby forming the backbone of further, microstructure-centered material development.

Influence of loading direction on mechanical behavior, microstructure characteristic and texture evolution of WE43-T4 magnesium alloy undergoing uniaxial compression

Abstract: To elucidate the influence of loading direction on deformation behavior of WE43-T4 magnesium (Mg) alloy, compressive tests, optical microstructure (OM), X-ray diffraction (XRD) and electron backscattered diffraction (EBSD) characterizations and Schmid factor (SF) analysis have been conducted in the present study. All samples (0° sample, 45° sample and 90° sample) exhibit minor plastic anisotropy during uniaxial compression at room temperature. The difference about 0.2% proof yield stress is closely related to various activities of prismatic slip, {10−12} extension twinning (ET) and {11−21} ET, while the difference about strain hardening response is attributable to various activities of basal slip. The solid solution of Y element strengthens the critical resolved shear stress of {10−12} ET harder than that of {11−21} ET, resulting in their synchronous activation in 8%-deformed samples. The SF values for {10−12} ET and {11−21} ET possess a decreasing tendency, resulting in a decreasing tendency about their activities from 0° sample to 90° sample. These activated ETs are mainly responsible for the observed texture with different degrees of concentration for c-axes of most grains towards compression direction. In these 20%-deformed samples, the formation of tilted basal texture is mainly associated with the activities of pyramidal slip and {10−11}-{10−12} double twinning.