scholarly journals In‐situ 4D tomography image analysis framework to follow sintering within 3D printed glass scaffolds

2021 ◽  
Author(s):  
A. I. Kondarage ◽  
G. Poologasundarampillai ◽  
A. Nommeots‐Nomm ◽  
P.D. Lee ◽  
T.D. Lalitharatne ◽  
...  
Keyword(s):  
Image Analysis ◽  
Analysis Framework ◽  
Tomography Image ◽  
3D Printed

scholarly journals Image analysis framework with focus evaluation for in situ characterisation of particle size and shape attributes

2018 ◽  
Vol 191 ◽  
pp. 208-231 ◽  
Author(s):  
Javier Cardona ◽  
Carla Ferreira ◽  
John McGinty ◽  
Andrew Hamilton ◽  
Okpeafoh S. Agimelen ◽  
...  
Keyword(s):  
Particle Size ◽  
Image Analysis ◽  
Analysis Framework ◽  
Size And Shape ◽  
Particle Size And Shape

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

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2575
Author(s):  
Hao Wen ◽  
Chang Huang ◽  
Shengmin Guo
Keyword(s):  
Image Analysis ◽  
Defect Detection ◽  
Original Video ◽  
Sem Images ◽  
Single Defect ◽  
Defect Image ◽  
3D Printed ◽  
Metallic Additive ◽  
Almost All

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.

scholarly journals Microchannelled alkylated chitosan sponge to treat noncompressible hemorrhages and facilitate wound healing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xinchen Du ◽  
Le Wu ◽  
Hongyu Yan ◽  
Zhuyan Jiang ◽  
Shilin Li ◽  
...  
Keyword(s):  
Wound Healing ◽  
Shape Recovery ◽  
Parenchymal Cell ◽  
Gelatin Sponge ◽  
Defect Model ◽  
Liver Parenchymal Cell ◽  
E Coli ◽  
Tissue Integration ◽  
3D Printed

AbstractDeveloping an anti-infective shape-memory hemostatic sponge able to guide in situ tissue regeneration for noncompressible hemorrhages in civilian and battlefield settings remains a challenge. Here we engineer hemostatic chitosan sponges with highly interconnective microchannels by combining 3D printed microfiber leaching, freeze-drying, and superficial active modification. We demonstrate that the microchannelled alkylated chitosan sponge (MACS) exhibits the capacity for water and blood absorption, as well as rapid shape recovery. We show that compared to clinically used gauze, gelatin sponge, CELOX™, and CELOX™-gauze, the MACS provides higher pro-coagulant and hemostatic capacities in lethally normal and heparinized rat and pig liver perforation wound models. We demonstrate its anti-infective activity against S. aureus and E. coli and its promotion of liver parenchymal cell infiltration, vascularization, and tissue integration in a rat liver defect model. Overall, the MACS demonstrates promising clinical translational potential in treating lethal noncompressible hemorrhage and facilitating wound healing.

An automatic analysis method for in situ hybridization using high-resolution image analysis

1989 ◽  
Vol 281 (5) ◽  
pp. 336-341 ◽  
Author(s):  
W. Stolz ◽  
K. Scharffetter ◽  
W. Abmayr ◽  
W. K�ditz ◽  
T. Krieg
Keyword(s):  
Image Analysis ◽  
In Situ Hybridization ◽  
High Resolution ◽  
Automatic Analysis ◽  
Analysis Method ◽  
Resolution Image ◽  
High Resolution Image
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