Surface roughness assessment on hole drilled through the identification and clustering of relevant external and internal signal statistical features

2022 ◽  
Vol 36 ◽  
pp. 143-157
Author(s):  
Aitor Duo ◽  
Rosa Basagoiti ◽  
Pedro J. Arrazola ◽  
Mikel Cuesta ◽  
Miren Illarramendi
Keyword(s):  
Surface Roughness ◽  
Statistical Features ◽  
Roughness Assessment ◽  
Internal Signal

scholarly journals Comparison of four technics of surface roughness assessment of corneal lamellar cuts

2017 ◽  
Vol 95 ◽  
Author(s):  
P. Gain ◽  
C. Jumelle ◽  
A. Hamri ◽  
G. Egaud ◽  
C. Mauclair ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Roughness Assessment

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 Process Capability Assessment Using Vision System

2021 ◽  
Vol 13 (2) ◽  
pp. 96-102
Author(s):  
Shivanna Dodda Mallappa ◽  
◽  
Kiran Mysore Bhaskar ◽  
Venkatesh Gude Subbaraya ◽  
Kavitha Shimoga Divakar ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Vision System ◽  
Research Work ◽  
Stable Process ◽  
Process Capability ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Measuring Surface ◽  
Roughness Assessment

Surface roughness assessment would help in predicting a component’s functionality. This clearly shows the significance of measuring the surface roughness of machined components. Thus, each machined component, depending upon its intended function, requires a certain surface finish. To predict the surface roughness of a machined component, a detailed understanding of the machining parameters is essential. This is because, surface roughness generated on a component, depends upon machining parameters speed, feed, and depth of cut. A stable manufacturing process gives a consistent surface finish on all the manufactured components. Thus, only by having a stable process, consistent quality of manufactured products is possible. The capability of the machine is defined as the capability of the machine to carry out the set process efficiently and effectively to produce parts as per the specification limits. Machining parameters, tools, coolant flow rate, etc. An effort has been made in this research work, to show how by measuring surface roughness of machined components process capability can be assessed. Thus, the method is a novel technique of assessing the process capability of a given process. A capable process would help a manufacturing company in meeting customer expectations. The proposed method is of non-contact type, quick, and industry-friendly.

scholarly journals Wire Roughness Assessment of 0.016″ × 0.022″ the Technique Lingual Orthodontics

2017 ◽  
Vol 18 (4) ◽  
pp. 295-299
Author(s):  
Fátima MM Facchini ◽  
Mario Vedovello Filho ◽  
Silvia AS Vedovello ◽  
Flávio A Cotrim ◽  
Andréa Cotrim-Ferreira ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Orthodontic Treatment ◽  
Statistical Test ◽  
Contact Mode ◽  
Lingual Orthodontics ◽  
Atomic Force ◽  
Roughness Assessment ◽  
The Difference ◽  
Orthodontic Archwires

ABSTRACT Objective To evaluate the difference in surface roughness of stainless steel archwires of different commercial brands used in lingual orthodontics. Materials and methods Precontoured arches measuring 0.016″ × 0.022″ were selected of the following brands: Tecnident, Adenta, G&H, Highland Metals Inc., Ormco, Incognito, and Ebraces. Quantitative evaluation of the surface roughness of archwires was performed by means of an atomic force microscope in contact mode. Three surface readouts were taken of each sample, analyzing areas of 20 × 20 μm. Each scan of the samples produced a readout of 512 lines, generating threedimensional images of the wires. The analysis of variance statistical test was applied to prove significant variables (p > 0.05), with H0 being rejected and H1 accepted. Results The Incognito brand showed the lowest surface roughness. The archwires of brands Adenta, Tecnident, Highland, and Ormco showed similar values among them, and all close to these obtained by the Incognito brand. The archwires of the Ebraces brand showed the highest surface roughness, with values being close to those of the G&H Brand. Conclusion There was a statistical difference in surface roughness of orthodontic archwires among the brands studied. Clinical significance Companies should pay attention to the quality control of their materials, as these may directly affect the quality of orthodontic treatment. How to cite this article Facchini FMM, Filho MV, Vedovello SAS, Cotrim FA, Cotrim-Ferreira A, Tubel CAM. Wire Roughness Assessment of 0.016″ × 0.022″ the Technique Lingual Orthodontics. J Contemp Dent Pract 2017;18(4):295-299.

Methods and approaches to the surface roughness assessment

2020 ◽  
Author(s):  
Vladimir Lyukshin ◽  
Dmitry Shatko ◽  
Pavel Strelnikov
Keyword(s):  
Surface Roughness ◽  
Roughness Assessment

scholarly journals Applying Terrestrial Laser Scanning for Soil Surface Roughness Assessment

2015 ◽  
Vol 7 (2) ◽  
pp. 2007-2045 ◽  
Author(s):  
Milutin Milenković ◽  
Norbert Pfeifer ◽  
Philipp Glira
Keyword(s):  
Surface Roughness ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Soil Surface ◽  
Soil Surface Roughness ◽  
Roughness Assessment
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