Gong Xinyi, Yabansu Yuksel C, Collins Peter C, Kalidindi Surya R
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0245, USA.
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA.
Materials (Basel). 2020 Oct 17;13(20):4641. doi: 10.3390/ma13204641.
Compositionally graded cylinders of Ti-Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of a-b microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress-strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17-1.78 mm, and beta content in the range of 20-83 vol.%. The estimated values of the Young's moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97-130 GPa and 828-1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables.
采用激光工程净成形(LENS™)技术制备了Ti-Mn合金成分梯度圆柱,沿圆柱轴线方向,Mn含量从0 wt.%变化到12 wt.%。对圆柱进行不同的后处理热处理,以制备大量α-β微观结构的样品库。利用扫描电子显微镜(SEM)中的背散射电子(BSE)成像研究样品库中的微观结构,并使用球形压痕应力-应变规程评估其力学性能。这些规程表明,微观结构呈现出平均弦长在0.17-1.78 mm范围内、β相含量在20-83 vol.%范围内的特征。通过球形压痕估计的杨氏模量和拉伸屈服强度值分别在97-130 GPa和828-1864 MPa范围内变化。发现LENS技术与球形压痕规程的联合使用有助于快速探索材料和工艺空间。通过高斯过程回归(GPR)对工艺条件、几个关键微观结构特征和测量的材料性能之间的相关性进行了分析。这些数据驱动的统计模型为这些变量之间的潜在相关性提供了有价值的见解。