The document summarizes several papers that studied surface roughness measurement using computer vision and image processing techniques. Various methodologies are discussed, including using Euclidean and Hamming distances to compare test and reference images, extracting mean gray levels from wavelet transforms to classify roughness, using vision systems and polynomial networks to measure roughness in turning processes, and analyzing wavelet packet transforms of cutting force signals to monitor roughness online. The studies demonstrated that vision-based techniques can provide alternatives to contact profilometry and achieve accurate roughness measurement and monitoring.

2. Author Worked on Methodology outcome
T. Jeyapoovan & M.
Murugan
Surface roughness
classification using image
processing
Euclidean distance and
Hamming distance of the
reference images and a test
surface image for comparison.
Using Euclidean distance and Hamming distance, the
matching of test images with reference images provided
excellent results. It was observed that the Euclidean and
Hamming distances were very low for surfaces with similar
surface roughness values. Therefore, this technique is
ideal for online surface characterization of machined surfaces.
H. H. Shahabi & M.
M. Ratnam
Noncontact roughness
measurement of turned
parts using machine vision
Vision based roughness
measurement
Vision-based roughness measurement has several
advantages over the conventional stylus method
Morala-Argüello et al. A evaluation of surface
roughness classes by
computer vision using
wavelet transform in the
frequency domain
The mean
value of gray levels for the
vertical detail sub-image
was extracted and used as
image descriptor for surface
roughness discrimination using
Artificial Neural Networks.
The method proposed offers an alternative to classic methods
based on the use of contact perthometers, and, therefore, it
permits to overcome the known limitations of these devices.

3. Author Worked on Methodology outcome
B. Y. Lee, et al. A Study of Computer
Vision for Measuring
Surface Roughness
in the Turning Process
A polynomial network is used to construct the
relationships between the cutting parameters
(cutting speed, federate, and depth of cut) and
cutting performance (surface roughness).
maximum absolute error between the surface
roughness measured by the vision system and that
measured by the stylus instrument is less than
11.32%.
Ghassan A. Al-
Kindia,& Bijan
Shirinzadehb (2007)
An evaluation of surface
roughness parameters
measurement using
vision-based data
Experimental tests and analysis were conducted
using data generated from the three alternative
techniques namely stylus-based data, vision-based
with ITC model data and vision-based with light
diffuse model data.
The ITC model showed to give more precise
measurements than the diffuse model using the
employed hardware. The overall accomplished
accuracy of all gained roughness parameters using
the ITC model, except the Rsk parameter, is within
15% of deviation in value in comparison with
relative stylus-based parameters, though certain
roughness parameters namely Sv, Rp, Rq, Rt, and
Rsm showed to achieve more close results than the
others.
E. García Plaza & P.J.
Núñez López(2018)
Analysis of cutting force
signals by wavelet packet
transform for surface
roughness monitoring in
CNC turning
In this study analysed the behaviour of 40 mother
wavelet functions for processing cutting force
signals to monitor surface roughness (Ra). The
wavelet packet transform (WPT) was applied to the
signals obtained for each orthogonal cutting
force component (Fx, Fy, Fz), with 5-level
decomposition (L1, . . ., L5) of the original signal
into approximation and detail packets Then, the
information provided by each packet was evaluated
for the prediction of surface finish using three
methods: global analysis of all the packets (G-WPT)
obtained at a decomposition level Lj, the analysis of
maximum energy packets (E-WPT), and the analysis
of maximum entropy packets (SE-WPT).
WPT method was observed to be an ideal
procedure for processing on-line cutting force
signals applied to the real-time monitoring of
surface finish, and was estimated to be highly
accurate and reliable, at a low
analyticalcomputational
cost, without the need for other types of signal,
nor static process parameters, such as cutting
conditions and/or tool geometry

4. Author Worked on Methodology outcome
E S Gadelmawla Estimation of surface
roughness for turning
operations using image
texture features
GLCM Correlation between ra and all texture features are have been
calculated.
Anna Zawada-
Tomkiewicz
Estimation of surface
roughness parameter
based on machined
surface image