Fundamental investigation on partially overlapped nano-cutting of monocrystalline germanium

2017 ◽  
Vol 49 ◽  
pp. 160-168 ◽  
Author(s):  
Min Lai ◽  
Xiaodong Zhang ◽  
Fengzhou Fang ◽  
Minghai Bi
Keyword(s):  
Fundamental Investigation ◽  
Monocrystalline Germanium

Fundamental Investigation of a Gas Circuit Breaker in University

2010 ◽  
Vol 130 (12) ◽  
pp. 1047-1052
Author(s):  
Hiroaki Toda ◽  
Masayuki Hikita ◽  
Hisatoshi Ikeda
Keyword(s):  
Circuit Breaker ◽  
Fundamental Investigation

Fundamental Investigation of a Grating Consisting of InSb-Coated Dielectric Cylinders on a Substrate in the THz Regime

2020 ◽  
Vol E103.C (11) ◽  
pp. 567-574 ◽  
Author(s):  
Jun SHIBAYAMA ◽  
Sumire TAKAHASHI ◽  
Junji YAMAUCHI ◽  
Hisamatsu NAKANO
Keyword(s):  
Fundamental Investigation

What Structural Features Make Porous Carbons Work for Redox-Enhanced Electrochemical Capacitors? A Fundamental Investigation

2021 ◽  
Vol 6 (3) ◽  
pp. 854-861
Author(s):  
Yang Zhao ◽  
Erin E. Taylor ◽  
Xudong Hu ◽  
Brian Evanko ◽  
Xiaojun Zeng ◽  
...  
Keyword(s):  
Electrochemical Capacitors ◽  
Structural Features ◽  
Porous Carbons ◽  
Fundamental Investigation

Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

2007 ◽  
Vol 329 ◽  
pp. 15-20 ◽  
Author(s):  
Xun Chen ◽  
James Griffin
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Material Removal ◽  
Grinding Process ◽  
Short Time Fourier Transform ◽  
Fundamental Investigation ◽  
Mechanical Behaviours ◽  
Wavelets Transform ◽  
Short Time ◽  
Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

Fundamental investigation of microsecond breakdown near a high permittivity dielectric

2015 ◽  
Author(s):  
S. Feathers ◽  
A. Fierro ◽  
S. Beeson ◽  
J. Stephens ◽  
J. Dickens ◽  
...  
Keyword(s):  
High Permittivity ◽  
Fundamental Investigation
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