scholarly journals Inversive Technique of Surface Roughness Estimation Using Multi-incident Angular Data by Microwave Scatterometer

1997 ◽  
Vol 41 ◽  
pp. 927-932
Author(s):  
Tosiyuki NAKAEGAWA ◽  
Shuhei SAEGUSA ◽  
Satoshi IKEDA ◽  
Katumi MUSIAKE ◽  
Masahiro KOIKE ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Microwave Scatterometer ◽  
Angular Data ◽  
Surface Roughness Estimation

scholarly journals Surface roughness estimation of MBE grown CdTe/GaAs(211)B by ex-situ spectroscopic ellipsometry

AIP Advances ◽  
2016 ◽  
Vol 6 (7) ◽  
pp. 075111 ◽  
Author(s):  
Merve Karakaya ◽  
Elif Bilgilisoy ◽  
Ozan Arı ◽  
Yusuf Selamet
Keyword(s):  
Surface Roughness ◽  
Spectroscopic Ellipsometry ◽  
Ex Situ ◽  
Surface Roughness Estimation

scholarly journals In-Process Surface Roughness Estimation Model for Compliant Abrasive Belt Machining Process

Procedia CIRP ◽  
2016 ◽  
Vol 46 ◽  
pp. 254-257 ◽  
Author(s):  
Vigneashwara Pandiyan ◽  
Tegoeh Tjahjowidodo ◽  
Meena Periya Samy
Keyword(s):  
Surface Roughness ◽  
Machining Process ◽  
Estimation Model ◽  
Abrasive Belt ◽  
Surface Roughness Estimation

Surface Roughness Estimation based on Vibration Measurement and Simplified Model

2019 ◽  
Vol 2019 (0) ◽  
pp. 606
Author(s):  
Takashi Misaka ◽  
Jonny Herwan ◽  
Oleg Ryabov ◽  
Seisuke Kano ◽  
Hiroyuki Sawada ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Vibration Measurement ◽  
Simplified Model ◽  
Surface Roughness Estimation

A Regression Model for Force and Surface Roughness Estimation during Hard Turning

2011 ◽  
Vol 299-300 ◽  
pp. 1167-1170 ◽  
Author(s):  
Gaurav Bartarya ◽  
S.K. Choudhury
Keyword(s):  
Surface Roughness ◽  
Hard Turning ◽  
Goodness Of Fit ◽  
Cutting Parameters ◽  
Anova Analysis ◽  
Aisi 52100 ◽  
Aisi 52100 Steel ◽  
Factorial Design Of Experiments ◽  
Full Factorial ◽  
Surface Roughness Estimation

Forces in Hard turning can be used to evaluate the performance of the process. Cutting parameters have their own influence on the cutting forces on the tool. The present work is an attempt to develop a force prediction model based on full factorial design of experiments for machining EN31 steel (equivalent to AISI 52100 steel) using uncoated CBN tool. The force and surface roughness regression models were developed using the data from various set of experiments with in the range of parameters selected. The predictions from the models were compared with the measured force and surface roughness values. The ANOVA analysis was undertaken to test the goodness of fit of data.

Flank Wear and Surface Roughness Estimation in Steel Turning

2012 ◽  
Vol 498 ◽  
pp. 115-120
Author(s):  
D. Rodríguez Salgado ◽  
I. Cambero ◽  
J.M. Herrera ◽  
F.J. Alonso
Keyword(s):  
Surface Roughness ◽  
Flank Wear ◽  
Computing Time ◽  
Research Community ◽  
Machining Process ◽  
Monitoring Systems ◽  
Motor Current ◽  
System 2 ◽  
Steel Turning ◽  
Surface Roughness Estimation

This paper presents a method to detect tool wear and surface roughness during steel dry turning. It is important to note that the objective of the proposed method is to fulfill the needs in the development of these monitoring systems according with the research community in this area, and they are: 1) A trade-off between the number of sensors used and their cost, and the performance of the monitoring system, 2) A sufficiently reduced computing time that allows to change the tool before the wear exceeds the fixed threshold and/or stop the machining process before the surface roughness exceeds its threshold, and 3) The use of sensors that do not disturb the machining process. The monitoring signals are the feed motor current and the vibrations. The results show that the proposed system can be used for this purpose due to its high accuracy and reliability.

scholarly journals Lunar Surface Roughness Estimation Using Stereoscopic Data

2009 ◽  
Vol 7 (ists26) ◽  
pp. Pk_29-Pk_34 ◽  
Author(s):  
Tomoyuki YOSHIMATSU ◽  
Akira IWASAKI ◽  
Junichi HARUYAMA ◽  
Makiko OHTAKE ◽  
Tsuneo MATSUNAGA
Keyword(s):  
Surface Roughness ◽  
Lunar Surface ◽  
Surface Roughness Estimation

scholarly journals Automatic snow surface roughness estimation using digital photos

2012 ◽  
Vol 58 (211) ◽  
pp. 993-1007 ◽  
Author(s):  
Terhikki Manninen ◽  
Kati Anttila ◽  
Tuure Karjalainen ◽  
Panu Lahtinen
Keyword(s):  
Surface Roughness ◽  
Control Point ◽  
Digital Camera ◽  
Snow Surface ◽  
Analysis Software ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Manual Intervention ◽  
Fully Automatic ◽  
Surface Roughness Estimation

AbstractA surface roughness measurement system for snow is presented. It is based on a background board with scales on the edges and a digital camera. Analysis software is developed for automatic processing of images to produce calibrated profiles. The image analysis and calibration was fully automatic in >99% of the studied cases. In the others, the intensity adjustment or board detection needed manual intervention. Profile detection, control point picking and calibration always worked autonomously. The accuracy of the system depends on the photographing configuration, and is typically of the order of 0.1 mm vertically and 0.04 mm horizontally. The method tolerates relatively well cases of snowfall, traces of wiping the black background dry, uneven shading, reflected sunlight, reflected flash light, litter on the snow surface and a tilted plate. The repeatability of the system is at least 1%.

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