D
Di Wu
Researcher at Shanghai University of Engineering Sciences
Publications - 43
Citations - 423
Di Wu is an academic researcher from Shanghai University of Engineering Sciences. The author has contributed to research in topics: Welding & Microstructure. The author has an hindex of 5, co-authored 32 publications receiving 85 citations.
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A Review on Additive Manufacturing of Pure Copper
TL;DR: In this paper, the current status of research on the structural design and preparation of complex pure copper parts by researchers using selective laser melting (SLM), selective electron beam melting (SEBM), and binder jetting (BJ) is reviewed.
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Microstructure and wide temperature range self-lubricating properties of laser cladding NiCrAlY/Ag2O/Ta2O5 composite coating
TL;DR: In this paper, Li et al. employed laser cladding for the preparation of NiCrAlY/Ag2O/Ta2O5 on Inconel 625 alloy substrate.
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A review on the effect of laser pulse shaping on the microstructure and hot cracking behavior in the welding of alloys
Peilei Zhang,Peilei Zhang,Zhiyuan Jia,Zhishui Yu,Haichuan Shi,Li Shaowei,Di Wu,Hua Yan,Xin Ye,Jieshi Chen,Fuxin Wang,Yingtao Tian +11 more
TL;DR: In this article, the application of pulsed laser in alloy welding is described and the main welding problems and prevention methods are summarized, the index had been proposed to predict the sensitivity of solidification cracks, the formation and growth processes of hot cracks in pulsed Laser welding was more comprehensively analyzed.
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In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion
TL;DR: This work developed an innovative welder intelligence-enhanced deep random forest fusion (WI-DRFF) approach, aiming to describe the dynamics of front-side molten pool and accurately predict the weld penetration and is a new paradigm in the digitization and intelligence of welding process.
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Visual-Acoustic Penetration Recognition in Variable Polarity Plasma Arc Welding Process Using Hybrid Deep Learning Approach
TL;DR: An end-to-end visual-acoustic penetration recognition (VAPR) framework based on a hybrid convolutional neural network (CNN) and extreme learning machine (ELM) and employed the ELM model as a strong tool to classify penetration status.