Z
Zhonghua Yu
Researcher at Zhejiang University
Publications - 16
Citations - 561
Zhonghua Yu is an academic researcher from Zhejiang University. The author has contributed to research in topics: Acoustic emission & Grinding. The author has an hindex of 10, co-authored 15 publications receiving 406 citations.
Papers
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Journal ArticleDOI
In situ monitoring of FDM machine condition via acoustic emission
Haixi Wu,Yan Wang,Zhonghua Yu +2 more
TL;DR: In this paper, a new method for in situ monitoring of FDM machine conditions, where acoustic emission (AE) technique is applied, is proposed, allowing for the identification of both normal and abnormal states of the machine conditions.
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Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model
Haixi Wu,Zhonghua Yu,Yan Wang +2 more
TL;DR: In this paper, a real-time lightweight AM machine condition monitoring approach is proposed, where acoustic emission (AE) sensor is used, and the original AE waveform signals are first simplified as AE hits, and then segmental and principal component analyses are applied to further reduce the data size and computational cost.
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Grinding wheel wear monitoring based on wavelet analysis and support vector machine
Zhensheng Yang,Zhonghua Yu +1 more
TL;DR: In this paper, a novel grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine is proposed, where the grinding signals are collected by an acoustic emission (AE) sensor.
Journal ArticleDOI
Application of Hilbert–Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process
TL;DR: In this article, a Hilbert-Huang transform (HHT) was applied as a signal processing tool to digest the raw acoustic emission and accelerator signals and to extract grinding burn features.
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Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission
Haixi Wu,Zhonghua Yu,Yan Wang +2 more
TL;DR: Results show that it is feasible to use the proposed method to diagnose the typical process failures, including both detection and identification, and could serve as a non-intrusive diagnostic tool for FFF process monitoring, and has the potential to be applied to other AM processes.