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Currently, no commercial aluminum 7000 series filaments are available for making aluminum parts using fused deposition modeling (FDM)-based additive manufacturing (AM). The key technical challenge associated with the FDM of aluminum alloy parts is consolidating the loosely packed alloy powders in the brown-body, separated by thin layers of surface oxides and polymer binders, into a dense structure. Classical pressing and sintering-based powder metallurgy (P/M) technologies are employed in this study to assist the development of FDM processing strategies for making strong Al7075 AM parts. Relevant FDM processing strategies, including green-body/brown-body formation and the sintering processes, are examined. The microstructures of the P/M-prepared, FDM-like Al7075 specimens are analyzed and compared with commercially available FDM 17-4 steel specimens. We explored the polymer removal and sintering strategies to minimize the pores of FDM-Al7075-sintered parts. Furthermore, the mechanisms that govern the sintering process are discussed.

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.

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.

Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.