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CoatingsVolume 13, Issue 2, February 2023, Article number 447

Effective Detection of the Machinability of Stainless Steel from the Aspect of the Roughness of the Machined Surface(Article)(Open Access)

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  • aMechanical Engineering Faculty in Slavonski Brod, University of Slavonski Brod, Slavonski Brod, 35000, Croatia
  • bFaculty of Technical Sciences, University of Novi Sad, Novi Sad, 21000, Serbia
  • cFaculty of Management, Comenius University Bratislava, Bratislava, 81499, Slovakia
  • dFaculty of Economics and Engineering Management, University Business Academy, Novi Sad, 21000, Serbia

Abstract

Reliable measurement of surface roughness (Ra) is extremely important for quality control of production processes. The cost of the equipment and the duration of the measurement process are very high. The aim of this work is to develop a device for non-destructive measurement of specific roughness levels on stainless steel using computer vision. The device should be structurally simple, affordable, accurate, and safe for practical use. The purpose of the device is to effectively detect the level of roughness of the treated surface obtained by the water jet cutting process. On the basis of the obtained results, it is possible to adjust the parameters during the cutting process. The principle of operation of the device is based on measuring the intensity of the visible spectrum of the light reflected from the surface of the sample to be measured and correlating these values with the values of the measured roughness. After testing several variants of the device, the so-called vertical measurement variant was developed using the following equipment: violet light LED, optical filter and light splitter, USB 2.0 web camera, Arduino microcontroller, personal computer, and LabView programming interface. © 2023 by the authors.

Author keywords

computer visionoptical measurementreflectionstainless steelsurface roughness
  • ISSN: 20796412
  • Source Type: Journal
  • Original language: English
  • DOI: 10.3390/coatings13020447
  • Document Type: Article
  • Publisher: MDPI

  Savković, B.; Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;
  Dudic, B.; Faculty of Management, Comenius University Bratislava, Bratislava, Slovakia;
© Copyright 2023 Elsevier B.V., All rights reserved.

Cited by 4 documents

Li, J. , Chen, M.
DEW-YOLO: An Efficient Algorithm for Steel Surface Defect Detection
(2024) Applied Sciences (Switzerland)
Ross, N.S. , Mashinini, P.M. , Sherin Shibi, C.
A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models
(2024) Measurement: Journal of the International Measurement Confederation
Sharun, V. , Ronald, B.A.
Optimization of Process Parameters in Abrasive Water Jet Machining of Austempered Ductile Iron (ADI)
(2024) Journal of Materials Engineering and Performance
View details of all 4 citations
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