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• A special surface grain was studied by LSR experiments and phase-field simulations. • The effects of thermal conditions on the surface microstructure were analyzed. • A selection map of surface grain morphology was established. • This study can give some new insights into the surface microstructure during LSR. During laser materials processing, the cooling rate (R) and thermal gradient (G) near the molten pool surface region are much higher than those in the traditional casting conditions, which may result in special solidification microstructures. In this study, the surface microstructure of near β titanium alloys was investigated by conducting both laser surface remelting experiments and phase-field simulations. It was found that the grains nucleated near the surface region of the molten pool exhibit near globular shape with 〈1 1 0〉-oriented side branches, which are very different from the ordinary dendrite morphology in casting conditions. Phase-field simulations were used to detect the formation mechanism of such special surface grain morphology. It was illustrated that the surface grain morphology is determined by the combined effect of high cooling rate (R) and high vertical thermal gradient (G). The high R results in the morphology transition from needle-like dendrite to near globular shape by increasing the undercooling and reducing the impact of anisotropy of interface energy. The high G promotes the grain contour to be more close to globular and facilitates the fins-like side branches to develop along 〈1 1 0〉 direction. Furthermore, a selection map of surface grain morphology at various thermal conditions (R and G) was established, which can be applied to predict the surface grain morphology. [ABSTRACT FROM AUTHOR]

[Display omitted] • A surrogate model for dendritic microstructures in solidification is established. • A rational way to choose the reduced-order features is proposed. • Including more reduced-ordered features may worsen the microstructure prediction. Establishing a rigorous quantitative relationship between solidification parameters and microstructure is critical for facilitating the fabrication of metallic components with desired properties. Conventional approaches are time and effort intensive and lack a rigorous framework for the systematic and accurate quantification of microstructure. In this study, a novel data science approach is applied to extract high-value, computationally low-cost process-structure linkages from phase field simulations of dendritic grain growth during solidification. Reduced-order measures, which are adequate for identifying the salient features of microstructures and evaluating the effects of solidification process parameters, are obtained through two-point statistics and principal component analysis. Building on these reduced-order measures, computationally efficient surrogate models are established using various regression methods. It is demonstrated that the surrogate model with the best predictive performance can provide good predictions of not only the reduced-order measures but also the two-point statistics. Moreover, it is found that some reduced-order measures exhibit very complex relations with process parameters, which results in poor prediction accuracy. Compared with discarding the features with inaccurately predicted values, including them may lead to worse recovery of higher-order microstructure statistics. These results highlight the importance of microstructure feature selection in the improvement of prediction accuracy. [ABSTRACT FROM AUTHOR]