scispace - formally typeset
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.

Papers
More filters
Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

A review on the effect of laser pulse shaping on the microstructure and hot cracking behavior in the welding of alloys

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.