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Abstract

Deposition defects like porosity, crack and lack of fusion in additive manufacturing process is a major obstacle to commercialization of the process. Thus, metallurgical microscopy analysis has been mainly conducted to optimize process conditions by detecting and investigating the defects. However, these defect detection methods indicate a deviation from the operator’s experience. In this study, artificial intelligence based YOLOv3 of object detection algorithm was applied to avoid the human dependency. The algorithm aims to automatically find and label the defects. To enable the aim, 80 training images and 20 verification images were prepared, and they were amplified into 640 training images and 160 verification images using augmentation algorithm of rotation, movement and scale down, randomly. To evaluate the performance of the algorithm, total loss was derived as the sum of localization loss, confidence loss, and classification loss. In the training process, the total loss was 8.672 for the initial 100 sample images. However, the total loss was reduced to 5.841 after training with additional 800 images. For the verification of the proposed method, new defect images were input and then the mean Average Precision (mAP) in terms of precision and recall was 0.3795. Therefore, the detection performance with high accuracy can be applied to industry for avoiding human errors.
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Bibliography

[1] O .H. Kwon, H.G. Kim, M.J. Ham, W.R. Kim, G.H. Kim, J.H. Cho, N.I. Kim, K.I. Kim, J. Intel. Manuf. 31, 375-386 (2020).
[2] L. Scime, J. Beuth, Addit. Manuf. 24, 273-286 (2018).
[3] L. Scime, J. Beuth, Addit. Manuf. 19, 114-126 (2018).
[4] L. Scime, J. Beuth, Addit. Manuf. 25, 151-165 (2019).
[5] L. Scime, J. Beuth, Addit. Manuf. 29, 100830, 1-9 (2019).
[6] M. Khanzadeh, W. Tian, A. Yadollahi, H.R. Doude, M.A. Tschopp, Addit. Manuf. 23, 443-456 (2018).
[7] M. Khanzadeh, S. Chowdhury, M. Marufuzzaman, M.A. Tschopp, L. Bian, J. Manuf. Syst. 47, 69-82 (2018).
[8] M. Khanzadeh, S. Chowdhury, M.A. Tschopp, H.R. Doude, M. Marufuzzaman, L. Bian, IISE Trans. 51, 5, 437-455 (2019)
[9] J . Redmon, A. Farhadi, arXiv preprint, 1804.02767 (2018).
[10] https://imageai.readthedocs.io/en/latest/
[11] https://github.com/tzutalin/labelImg
[12] http://www.image-net.org/
[13] https://imgaug.readthedocs.io/en/latest/index.html
[14] J .S. Kim, B.J. Kang, S.W. Lee, J. Mech. Sci. Technol. 33, 12, 1-7 (2019).
[15] A. Torralba, A.A. Efros, Proc. CVPR IEEE 12218709, 1521-1528 (2011).
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Authors and Affiliations

Byungjoo Choi
1
ORCID: ORCID
Yongjun Choi
1
ORCID: ORCID
Moon Gu Lee
1
ORCID: ORCID
Jung Sub Kim
2
ORCID: ORCID
Sang Won Lee
2
ORCID: ORCID
Yongho Jeon
1
ORCID: ORCID

  1. Ajou University, Department of Mechanical Engineering, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi 16499, Republic of Korea
  2. Sungkyunkwan University School of Mechanical Engineering, Suwon, Republic of Korea
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Abstract

Marine sediments with rapid oxic/anoxic transitions are difficult to monitor in real time. Organic overload that may lead to anoxia and buildup of hydrogen sulfide can be caused by a variety of factors such as sewage spills, harbor water stagnation, algal blooms and the vicinity of aquaculture operations. We have tested a novel multiprobe technology (named SPEAR) on marine sediments to evaluate its performance in monitoring sediments and overlaying water. Our results show the ability of the SPEAR probes to distinguish electrochemical changes at 2-3 mm scale and at hourly cycles. SPEAR probes have the ability to identify redox interfaces and redox transition zones in sediments, but do not use micromanipulators (which are cumbersome in field and underwater applications). We propose that the best target habitats for SPEAR-type monitoring are rapidly evolving muddy deposits and sediments near aquaculture operations where pollution with organics stresses the ecosystem.
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Authors and Affiliations

R. Popa
1
ORCID: ORCID
I.C. Moga
1
ORCID: ORCID
K.H. Nealson
2
ORCID: ORCID
V.M. Cimpoiasu
3
ORCID: ORCID

  1. DFR Systems SRL, R&D Department, Bucharest, Romania
  2. University of Southern California, Los Angeles, 90089, USA
  3. University of Craiova, Biology and Environmental Engineering Department, Frontier Biology and Astrobiology Research Center, Craiova, 200585, Romania

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