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Im Doo Jung
Researcher at Handong Global University
Publications - 42
Citations - 342
Im Doo Jung is an academic researcher from Handong Global University. The author has contributed to research in topics: Selective laser melting & Computer science. The author has an hindex of 6, co-authored 29 publications receiving 167 citations. Previous affiliations of Im Doo Jung include Ulsan National Institute of Science and Technology & Pohang University of Science and Technology.
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Insights into morphological evolution and cycling behaviour of lithium metal anode under mechanical pressure
Xuesong Yin,Wei Tang,Im Doo Jung,Kia Chai Phua,Stefan Adams,Seok Woo Lee,Guangyuan Wesley Zheng,Guangyuan Wesley Zheng +7 more
TL;DR: In this article, the authors investigated the effect of external pressure on the electrochemical deposition of lithium metal and found that a much more compact Li deposition can be achieved when a pressure is applied to the batteries in the charge/discharge processes.
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Microstructural effects on the tensile and fracture behavior of selective laser melted H13 tool steel under varying conditions
TL;DR: In this paper, a microstructural-mechanical correlative study has been conducted for characterization of selective laser melted H13 tool steel, where columnar microstructures are mostly composed of martensite with small amount of retained austenite.
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A comprehensive viscosity model for micro magnetic particle dispersed in silicone oil
TL;DR: In this paper, a multiplied form of phenomenological models taking the effect of shear rate, powder volume fraction, temperature and magnetic flux density was developed for the precise control of the magnetorheological fluid.
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Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels
Im Doo Jung,Da Seul Shin,Doohee Kim,Jungsub Lee,Min Sik Lee,Hye Jin Son,N.S. Reddy,Moobum Kim,Seung Ki Moon,Kyung Tae Kim,Ji-Hun Yu,Sangshik Kim,Seong Jin Park,Hyokyung Sung +13 more
TL;DR: In this paper, the information on microstructural volume fraction is utilized for the prediction of tensile strength, yield strength, and yield ratio via artificial neural networks, and the effects of each microstructure on the three mechanical properties were successfully predicted by employing back-propagation linear regression.
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Two-Phase Master Sintering Curve for 17-4 PH Stainless Steel
TL;DR: In this article, a two-phase master sintering curve model (MSC) was proposed to predict the activation energy of 17-4 PH stainless steel powders using the mean residual method.