In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision

2008 ◽  
Vol 40 (11-12) ◽  
pp. 1148-1157 ◽  
Author(s):  
H. H. Shahabi ◽  
M. M. Ratnam
Keyword(s):  
Surface Roughness ◽  
Machine Vision ◽  
Cycle Monitoring ◽  
Turned Parts

Effect of surface lay in the surface roughness evaluation using machine vision

Optik ◽  
2014 ◽  
Vol 125 (15) ◽  
pp. 3954-3960 ◽  
Author(s):  
Srinagalakshmi Nammi ◽  
B. Ramamoorthy
Keyword(s):  
Surface Roughness ◽  
Machine Vision ◽  
Roughness Evaluation ◽  
Effect Of Surface

Machine vision method for non-contact measurement of surface roughness of a rotating workpiece

Sensor Review ◽  
2015 ◽  
Vol 35 (1) ◽  
pp. 10-19 ◽  
Author(s):  
B. M. Kumar ◽  
M. M. Ratnam
Keyword(s):  
Surface Roughness ◽  
Machine Vision ◽  
Edge Detection ◽  
Vision System ◽  
Contact Method ◽  
Moment Invariant ◽  
Content Type ◽  
Mean Differences ◽  
The Moment ◽  
Rotating Workpiece

Purpose – This paper aims to propose a non-contact method using machine vision for measuring the surface roughness of a rotating workpiece at speeds of up to 4,000 rpm. Design/methodology/approach – A commercial digital single-lens-reflex camera with high shutter speed and backlight was used to capture a silhouette of the rotating workpiece profile. The roughness profile was extracted at sub-pixel accuracy from the captured images using the moment invariant method of edge detection. The average (Ra), root-mean square (Rq) and peak-to-valley (Rt) roughness parameters were measured for ten different specimens at spindle speeds of up to 4,000 rpm. The roughness values measured using the proposed machine vision system were verified using the stylus profilometer. Findings – The roughness values measured using the proposed method show high correlation (up to 0.997 for Ra) with those determined using the profilometer. The mean differences in Ra, Rq and Rt between the two methods were only 4.66, 3.29 and 3.70 per cent, respectively. Practical implications – The proposed method has significant potential for application in the in-process roughness measurement and tool condition monitoring from workpiece profile signature during turning, thus, obviating the need to stop the machine. Originality/value – The machine vision method combined with sub-pixel edge detection has not been applied to measure the roughness of a rotating workpiece.

Machine Vision for Surface Roughness Assessment of Inclined Components

2010 ◽  
Vol 437 ◽  
pp. 141-144 ◽  
Author(s):  
P. Priya ◽  
B. Ramamoorthy
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Machine Vision ◽  
Roughness Parameters ◽  
Grey Level ◽  
Ideal Orientation ◽  
Roughness Assessment ◽  
Occurrence Matrix ◽  
Made In ◽  
The Ideal

Many researchers have so far used machine vision and digital image processing for grabbing images of machined surfaces, improving their quality by pre-processing and then analysed them for evaluation of surface finish with a reasonable success. An attempt has been made in this work to capture the images of the surfaces with varying inclinations covering both the sides. The ideal orientation of the surface (flat and horizontal) is found by observing the variation in optical roughness parameters estimated from the grey level co-occurrence matrix as the angle of inclination changes. It is observed that the variation of roughness parameters with respect to angle of inclination also depends on the surface roughness of the component. The optical roughness values obtained by machine vision approach are then subsequently compared with the conventional Ra as obtained by stylus method and the analysis is presented.

scholarly journals Evolutionary Design of a System for Online Surface Roughness Measurements

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1904
Author(s):  
Valentin Koblar ◽  
Bogdan Filipič
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Machine Vision ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Paper Machine ◽  
Machine Learning Algorithm ◽  
Key Characteristics ◽  
Evolutionary Optimization Algorithms ◽  
Vision Algorithm

Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely applied, it has several drawbacks, such as limited measurement speed, sensitivity to vibrations, and requirement for precise positioning of the measured samples. In this paper, machine vision, machine learning and evolutionary optimization algorithms are used to induce a model for predicting the surface roughness of automotive components. Based on the attributes extracted by a machine vision algorithm, a machine learning algorithm generates the roughness predictive model. In addition, an evolutionary algorithm is used to tune the machine vision and machine learning algorithm parameters in order to find the most accurate predictive model. The developed methodology is comparable to the existing contact measurement method with respect to accuracy, but advantageous in that it is capable of predicting the surface roughness online and in real time.

Surface Roughness Estimation of Turned Parts from Optical Image Measurements and Wavelet Decomposition

2014 ◽  
Vol 1 (1) ◽  
pp. 48-72
Author(s):  
R. Kamguem ◽  
A. S. Tahan ◽  
V. Songmene
Keyword(s):  
Surface Roughness ◽  
Vision System ◽  
A Priori ◽  
Measuring Method ◽  
Statistical Characteristics ◽  
Machining Parameters ◽  
Roughness Parameters ◽  
Significant Information ◽  
Turned Parts ◽  
The Impact

The surface roughness is very significant information required for product quality on the field of mechanical engineering and manufacturing, especially in aeronautic. Its measurement must therefore be conducted with care. In this work, a measuring method of the surface roughness based on machine vision was studied. The authors' use algorithms to evaluate new discriminatory features thereby than the statistical characteristics using the coefficients of the wavelet transform and used to estimate the roughness parameters. This vision system allows measuring simultaneously several parameters of the roughness at the same time, order to meet for the desired surface function used. The results were validated on three different families of materials: aluminum, cast iron and brass. The impact of material on the quality of the results was analyzed, leading to the development of multi-materials. The study had shown that several roughness parameters can be estimated using only features extracted from the image and a neural network without a priori knowledge of the machining parameters.

Sign in / Sign up

Export Citation Format

Share Document