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.

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In situ real time defect detection of 3D printed parts

Additive Manufacturing ◽

10.1016/j.addma.2017.08.003 ◽

2017 ◽

Vol 17 ◽

pp. 135-142 ◽

Cited By ~ 24

Author(s):

Oliver Holzmond ◽

Xiaodong Li

Keyword(s):

Real Time ◽

Defect Detection ◽

3D Printed

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Analytical modelling of in situ layer-wise defect detection in 3D-printed parts: additive manufacturing

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06241-6 ◽

2020 ◽

Vol 111 (7-8) ◽

pp. 2311-2321

Author(s):

Oluwole K. Bowoto ◽

Bankole I. Oladapo ◽

S. A. Zahedi ◽

Francis T. Omigbodun ◽

Omonigho P. Emenuvwe

Keyword(s):

Additive Manufacturing ◽

Defect Detection ◽

Analytical Modelling ◽

3D Printed

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

Journal of the American Ceramic Society ◽

10.1111/jace.18182 ◽

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

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Correlation of Pore Structure and Permeability by SEM-Image Analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100180896 ◽

1990 ◽

Vol 48 (1) ◽

pp. 428-429

Author(s):

C. A. Callender ◽

Wm. C. Dawson ◽

J. J. Funk

Keyword(s):

Image Analysis ◽

Pore Structure ◽

Imaging System ◽

Pore Space ◽

Simulation Models ◽

Image Analyzer ◽

Imaging Data ◽

Sem Images ◽

Thin Sections ◽

Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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Diversity of Biodeteriorative Bacterial and Fungal Consortia in Winter and Summer on Historical Sandstone of the Northern Pergola, Museum of King John III’s Palace at Wilanow, Poland

Applied Sciences ◽

10.3390/app11020620 ◽

2021 ◽

Vol 11 (2) ◽

pp. 620

Author(s):

Magdalena Dyda ◽

Agnieszka Laudy ◽

Przemyslaw Decewicz ◽

Krzysztof Romaniuk ◽

Martyna Ciezkowska ◽

...

Keyword(s):

Seasonal Changes ◽

Microbial Community Composition ◽

Fungal Communities ◽

Bacterial Strains ◽

Microbial Biodiversity ◽

Acidophilic Bacteria ◽

Almost All ◽

Generation Sequencing ◽

King John

The aim of the presented investigation was to describe seasonal changes of microbial community composition in situ in different biocenoses on historical sandstone of the Northern Pergola in the Museum of King John III’s Palace at Wilanow (Poland). The microbial biodiversity was analyzed by the application of Illumina-based next-generation sequencing methods. The metabarcoding analysis allowed for detecting lichenized fungi taxa with the clear domination of two genera: Lecania and Rhinocladiella. It was also observed that, during winter, the richness of fungal communities increased in the biocenoses dominated by lichens and mosses. The metabarcoding analysis showed 34 bacterial genera, with a clear domination of Sphingomonas spp. across almost all biocenoses. Acidophilic bacteria from Acidobacteriaceae and Acetobacteraceae families were also identified, and the results showed that a significant number of bacterial strains isolated during the summer displayed the ability to acidification in contrast to strains isolated in winter, when a large number of isolates displayed alkalizing activity. Other bacteria capable of nitrogen fixation and hydrocarbon utilization (including aromatic hydrocarbons) as well as halophilic microorganisms were also found. The diversity of organisms in the biofilm ensures its stability throughout the year despite the differences recorded between winter and summer.

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In-situ deformation measurement of Zircaloy-4 cladding tube under various transient heating conditions using optical image analysis

Nuclear Engineering and Design ◽

10.1016/j.nucengdes.2020.110859 ◽

2020 ◽

Vol 370 ◽

pp. 110859

Author(s):

Gyeong-Ha Choi ◽

Dong-Hyun Kim ◽

Chang-Hwan Shin ◽

Jae Yong Kim ◽

Byoung Jae Kim

Keyword(s):

Image Analysis ◽

Optical Image ◽

Deformation Measurement ◽

Transient Heating ◽

In Situ Deformation

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

Nature Communications ◽

10.1038/s41467-021-24972-2 ◽

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.

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Pyrite (FeS2)-supported ultrafiltration system for removal of mercury (II) from water

Emergent Materials ◽

10.1007/s42247-021-00282-7 ◽

2021 ◽

Author(s):

Dong Suk Han ◽

Kawsher M. D. Solayman ◽

Ho Kyong Shon ◽

Ahmed Abdel-Wahab

Keyword(s):

Membrane Filter ◽

Pyrite Surface ◽

Ph Change ◽

Filtration Process ◽

Sem Images ◽

Flux Decline ◽

Cake Layer ◽

Water Effects ◽

Almost All ◽

Over Time

AbstractThis study investigated the Hg(II) removal efficiencies of the reactive adsorbent membrane (RAM) hybrid filtration process, a removal process that produces stable final residuals. The reaction mechanism between Hg(II) and pyrite and the rejection of the solids over time were characterized with respect to flux decline, pH change, and Hg and Fe concentration in permeate water. Effects of the presence of anions (Cl−, SO42−, NO3−) or humic acid (HA) on the rejection of the Hg(II)-contacted pyrite were studied. The presence of both HA and Hg(II) increased the rate of flux decline due to the formation of irreversible gel-like compact cake layers as shown in the experimental data and modeling related to the flux decline and the SEM images. Stability experiments of the final residuals retained on the membrane using a thiosulfate solution (Na2S2O3) show that the Hg(II)-laden solids were very stable due to little or no detection of Hg(II) in the permeate water. Experiment on the possibility of continuously removing Hg(II) by reusing the Hg/pyrite-laden membrane shows that almost all Hg(II) was adsorbed onto the pyrite surface regardless of the presence of salts or HA, and the Hg(II)-contacted pyrite residuals were completely rejected by the DE/UF system. Therefore, a membrane filter containing pyrite-Hg(II) could provide another reactive cake layer capable of further removal of Hg(II) without post-chemical treatment for reuse.

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Intelligent Recognition Model of Hot Rolling Strip Edge Defects Based on Deep Learning

Metals ◽

10.3390/met11020223 ◽

2021 ◽

Vol 11 (2) ◽

pp. 223

Author(s):

Dongcheng Wang ◽

Yanghuan Xu ◽

Bowei Duan ◽

Yongmei Wang ◽

Mingming Song ◽

...

Keyword(s):

Hot Rolling ◽

Detection Accuracy ◽

Recognition Time ◽

Common Edge ◽

Edge Defect ◽

Recognition Model ◽

Single Defect ◽

Rolling Strip ◽

Defect Image ◽

Edge Defects

The edge of a hot rolling strip corresponds to the area where surface defects often occur. The morphologies of several common edge defects are similar to one another, thereby leading to easy error detection. To improve the detection accuracy of edge defects, the authors of this paper first classified the common edge defects and then made a dataset of edge defect images on this basis. Subsequently, edge defect recognition models were established on the basis of LeNet-5, AlexNet, and VggNet-16 by using a convolutional neural network as the core. Through multiple groups of training and recognition experiments, the model’s accuracy and recognition time of a single defect image were analyzed and compared with recognition models with different learning rates and sample batches. The experimental results showed that the recognition model based on the AlexNet had a maximum accuracy of 93.5%, and the average recognition time of a single defect image was 0.0035 s, which could meet the industry requirement. The research results in this paper provide a new method and thought for the fine detection of edge defects in hot rolling strips and have practical significance for improving the surface quality of hot rolling strips.

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