Engineering topics

Guo, Shengmin (ORCID:0000000236730836)

Publications and source records attributed to Guo, Shengmin (ORCID:0000000236730836).

5 records

DOE OSTI2024

Study of Helium Irradiation Effect on Al6061 Alloy Fabricated by Additive Friction Stir Deposition

Additive friction stir deposition (AFS-D) is considered a productive method of additive manufacturing (AM) due to its ability to produce dense mechanical parts at a faster deposition rate compared to other AM methods. Al6061 alloy finds extensive application in aerospace and nuclear engineering; nevertheless, exposure to radiation or high-energy particles over time tends to deteriorate their mechanical performance. However, the effect of radiation on the components manufactured using the AFS-D method is still unexamined. In this work, samples from the as-fabricated Al6061 alloy, by AFS-D, and the Al6061 feedstock rod were irradiated with He+ ions to 10 dpa at ambient temperature. The microstructural and mechanical changes induced by irradiation of He+ were examined using a scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), and nanoindentation. This study demonstrates that, at 10 dpa of irradiation damage, the feedstock Al6061 produced a bigger size of He bubbles than the AFS-D Al6061. Nanoindentation analysis revealed that both the feedstock Al6061 and AFS-D Al6061 samples have experienced radiation-induced hardening. These studies provide a valuable understanding of the microstructural and mechanical performance of AFS-D materials in radiation environments, offering essential data for the selection of materials and processing methods for potential application in aerospace and nuclear engineering.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Computational exploration of biomedical HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr refractory high-entropy alloys

Refractory high entropy alloys (RHEAs) have been proven to be a potential candidate in the biomedical field due to their balanced mechanical properties and biocompatible composition. Recent experimental findings show that RHEAs like HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr have good mechanical properties such as high polarization and wear resistance than others which establish them as potential materials for biomedical application. In this work, we performed first-principles density functional theory calculations on the mechanical and thermal properties of HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr. The predicted lattice constant, density, Young's modulus, and Vickers hardness are consistent with the available experimental report, which verifies the accuracy of the applied model. The thermal coefficient of linear expansion of both RHEAs has been investigated by utilizing the Debye theory. The present methods could be applied to study other future RHEAs on exploration of their physical properties.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Performance of Carbide Alloy Compounds in Carbon Doped MoNbTaW

In this work, the performance of the carbon doped compositionally complex alloy (CCA) MoNbTaW was studied under ambient and high pressure and high temperature conditions. TaC and NbC carbides were formed when a large concentration of carbon was introduced while synthesizing the MoNbTaW alloy. Both FCC carbides and BCC CCA phases were detected in the sample compound at room temperature, in which the BCC phase was believed to have only refractory elements MoNbTaW while FCC carbide came from TaC and NbC. Carbides in the carbon doped MoNbTaW alloy were very stable since no phase transition was obtained even under 3.1 GPa and 870 °C by employing the resistor-heating diamond anvil cell (DAC) synchrotron X-ray diffraction technique. Via in situ examination, this study confirms the stability of carbides and MoNbTaW in the carbon doped CCA even under high pressure and high temperature.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗