A ground-based work of droplet deposition manufacturing toward microgravity: Fine pileup of horizontally ejected metal droplets on vertical substrates

The prevalence of nonalcoholic fatty liver disease (NAFLD) has been rapidly increasing worldwide. A choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) has been used to create a mouse model of nonalcoholic steatohepatitis (NASH). There are some reports on the effects on mice of being fed a CDAHFD for long periods of 1 to 3 months. However, the effect of this diet over a short period is unknown. Therefore, we examined the effect of 1-week CDAHFD feeding on the mouse liver. Feeding a CDAHFD diet for only 1-week induced lipid droplet deposition in the liver with increasing activity of liver-derived enzymes in the plasma. On the other hand, it did not induce fibrosis or cirrhosis. Additionally, it was demonstrated that CDAHFD significantly impaired mitochondrial respiration with severe oxidative stress to the liver, which is associated with a decreasing mitochondrial DNA copy number and complex proteins. In the gene expression analysis of the liver, inflammatory and oxidative stress markers were significantly increased by CDAHFD. These results demonstrated that 1 week of feeding CDAHFD to mice induces steatohepatitis with mitochondrial dysfunction and severe oxidative stress, without fibrosis, which can partially mimic the early stage of NASH in humans.

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In-situ monitoring for liquid metal jetting using a millimeter-wave impedance diagnostic

Scientific Reports ◽

10.1038/s41598-020-79266-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Tammy Chang ◽

Saptarshi Mukherjee ◽

Nicholas N. Watkins ◽

David M. Stobbe ◽

Owen Mays ◽

...

Keyword(s):

Liquid Metal ◽

Millimeter Wave ◽

Manufacturing Systems ◽

Wave Impedance ◽

In Situ Monitoring ◽

Full Wave ◽

Drop On Demand ◽

The Time Domain ◽

Metal Droplets

AbstractThis article presents a millimeter-wave diagnostic for the in-situ monitoring of liquid metal jetting additive manufacturing systems. The diagnostic leverages a T-junction waveguide device to monitor impedance changes due to jetted metal droplets in real time. An analytical formulation for the time-domain T-junction operation is presented and supported with a quasi-static full-wave electromagnetic simulation model. The approach is evaluated experimentally with metallic spheres of known diameters ranging from 0.79 to 3.18 mm. It is then demonstrated in a custom drop-on-demand liquid metal jetting system where effective droplet diameters ranging from 0.8 to 1.6 mm are detected. Experimental results demonstrate that this approach can provide information about droplet size, timing, and motion by monitoring a single parameter, the reflection coefficient amplitude at the input port. These results show the promise of the impedance diagnostic as a reliable in-situ characterization method for metal droplets in an advanced manufacturing system.

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Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽

10.3390/met11040639 ◽

2021 ◽

Vol 11 (4) ◽

pp. 639

Author(s):

Chen Ma ◽

Haifei Dang ◽

Jun Du ◽

Pengfei He ◽

Minbo Jiang ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Manufacturing Process ◽

Support Vector ◽

Defect Classification ◽

Droplet Deposition ◽

Network Support ◽

Visual Sensing ◽

Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

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