The influence of workpiece surface texture on visual measurement of roughness
-
Huaian Yi
Huaian Yi is an associate professor in the College of Mechanical and Control Engineering at Guilin University of Technology, and his main research area is machine vision.
,
Lingli Lu
Lingli Lu is a postgraduate student in the College of Mechanical and Control Engineering at Guilin University of Technology. Her main research area is machine vision.
Aihua Shu is a teacher in the College of Foreign Languages of Guilin University of Technology.
Jianhua Qin is an associate professor in the College of Mechanical and Control Engineering at Guilin University of Technology, and his main interests area are robotics, TCM information detection and processing.
Enhui Lu is a teacher in the College of Mechanical Engineering at Yangzhou University, China. His main research interests are machine vision and artificial intelligence algorithms for surface quality assessment.
Abstract
In view of the universal applicability of color information index and the ability of spectrum index to measure small roughness, this paper studies the influence of machined surface texture on roughness machine-vision measurement. Based on the micro-topography of different types of texture surface, the correlations between the average color difference, five typical spectral indices and the surface roughness parameters of two types of texture representative workpiece milling and grinding are focused on in the discussion. On this basis, the influences of the angle of light source and camera relative to the surface normal of the sample and the sample surface texture direction on roughness measurement are further discussed, and the reasons for the difference in index to characterize different process roughness are analyzed from the mechanism. The experimental results show that the different surface textures have different effects on light scattering, which in turn affects the sensitivity of the index to the roughness parameters.
Zusammenfassung
In Anbetracht der universellen Anwendbarkeit des Farbinformationsindexes und der Fähigkeit des Spektralindexes, kleine Rauheiten zu messen, wird in diesem Beitrag der Einfluss der bearbeiteten Oberflächentextur auf die maschinelle Rauheitsmessung untersucht. Ausgehend von der Mikrotopographie verschiedener Texturoberflächen werden die Korrelationen zwischen der durchschnittlichen Farbdifferenz, fünf typischen Spektralindizes und den Oberflächenrauheitsparametern von zwei für die Textur repräsentativen Werkstücken beim Fräsen und Schleifen in der Diskussion. Auf dieser Grundlage werden die Einflüsse des Winkels der Lichtquelle und der Kamera relativ zur Oberflächennormalen der Probe und der Richtung der Oberflächenstruktur der Probe auf die Rauheitsmessung weiter diskutiert, und die Gründe für den Unterschied im Index zur Charakterisierung verschiedener Prozessrauhigkeit werden anhand des Mechanismus analysiert. Die experimentellen Ergebnisse zeigen, dass die verschiedenen Oberflächenstrukturen unterschiedliche Auswirkungen auf die Lichtstreuung haben, was wiederum die Empfindlichkeit des Indexes gegenüber den Parameter beeinflusst.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 52065016
Award Identifier / Grant number: 2018GXNSFAA138154
Funding source: Guilin University of Technology
Award Identifier / Grant number: GLUTQD2017060
Funding statement: This work was supported by the National Natural Science Foundation of China (Grant No. 52065016), Guangxi Science and Technology Plan Project (Grant No. 2018GXNSFAA138154), and Doctor start-up funds of Guilin University of Technology (Grant No. GLUTQD2017060).
About the authors
Huaian Yi is an associate professor in the College of Mechanical and Control Engineering at Guilin University of Technology, and his main research area is machine vision.
Lingli Lu is a postgraduate student in the College of Mechanical and Control Engineering at Guilin University of Technology. Her main research area is machine vision.
Aihua Shu is a teacher in the College of Foreign Languages of Guilin University of Technology.
Jianhua Qin is an associate professor in the College of Mechanical and Control Engineering at Guilin University of Technology, and his main interests area are robotics, TCM information detection and processing.
Enhui Lu is a teacher in the College of Mechanical Engineering at Yangzhou University, China. His main research interests are machine vision and artificial intelligence algorithms for surface quality assessment.
References
1. Haralick RM, Shanmugam K and Dinstein I 1973 Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. SMC-3(6): 610–621.10.1109/TSMC.1973.4309314Search in Google Scholar
2. Weszka JS, Dyer CR and Rosenfeld A 1976 A comparative study of texture measures for terrain classification. IEEE Transactions on Systems, Man, and Cybernetics. SMC-6(4): 269–285.10.1109/TSMC.1976.5408777Search in Google Scholar
3. Luk F, Huynh V and North W 1989 Measurement of surface roughness by a machine vision system. Journal of Physics E: Scientific Instruments. 22: 977–980.10.1088/0022-3735/22/12/001Search in Google Scholar
4. Ramamoorthy B and Radhakrishnan V 1993 Statistical approaches to surface texture classification. Wear. 167(2): 155–161.10.1016/0043-1648(93)90320-LSearch in Google Scholar
5. Kumar R, Kulashekar P, Dhanasekar B and Ramamoorthy B 2005 Application of digital image magnification for surface roughness evaluation using machine vision. International Journal of Machine Tools & Manufacture. 45: 228–234.10.1016/j.ijmachtools.2004.07.001Search in Google Scholar
6. Liu XL, Wu CY, Liu YZ, Yan FG, Li YF and Wang P 2008 Detection of grinding surface’s quality based on image technique. Key Engineering Materials. 359–360: 499–503.10.4028/0-87849-459-6.499Search in Google Scholar
7. Al-Kindi GA and Shirinzadeh B 2009 Feasibility assessment of vision-based surface roughness parameters acquisition for different types of machined specimens. Image and Vision Computing. 27: 444–458.10.1016/j.imavis.2008.06.011Search in Google Scholar
8. Kamguem R, Tahan SA and Songmene V 2013 Evaluation of machined part surface roughness using image texture gradient factor. International Journal of Precision Engineering and Manufacturing. 14: 183–190.10.1007/s12541-013-0026-xSearch in Google Scholar
9. Venkat Ramana K and Ramamoorthy B 1996 Statistical methods to compare the texture features of machined surfaces. Pattern Recognition. 29(9): 1447–1459.10.1016/0031-3203(96)00008-8Search in Google Scholar
10. Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM and Soliman HH 2001 Using the grey level histogram to distinguish between roughness of surfaces. Proceedings of the Institution of Mechanical Engineers. Part B: Engineering Manufacture. 215(4): 545–553.10.1243/0954405011518494Search in Google Scholar
11. Gadelmawla ES 2004 A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT & E. International. 37: 577–588.10.1016/j.ndteint.2004.03.004Search in Google Scholar
12. Lu RS, Tian GY, Gledhill D and Ward S 2006 Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture. Applied Optics. 45: 1–9.10.1364/AO.45.008839Search in Google Scholar
13. Tsai DM, Chen JJ and Chen JF 1998 A vision system for surface roughness assessment using neural networks. Int. J. Adv. Manuf. Technol. 14: 412–422.10.1007/BF01304620Search in Google Scholar
14. Demircioglu P, Bogrekci I and Durakbasa NM 2013 Micro scale surface texture characterization of technical structures by computer vision. Measurement. 46: 2022–2028.10.1016/j.measurement.2013.02.012Search in Google Scholar
15. Lee BY, Yu SF and Juan H 2004 The model of surface roughness inspection by vision system in turning. Mechatronics. 14: 129–141.10.1016/S0957-4158(02)00096-XSearch in Google Scholar
16. Dhanasekar B and Ramamoorthy B 2008 Assessment of surface roughness based on super resolution reconstruction algorithm. Int. J. Adv. Manuf. Technol. 35: 1191–1205.10.1007/s00170-006-0799-5Search in Google Scholar
17. Yi HA, Liu J, Lu EH and Ao P 2016 Measuring grinding surface roughness based on the sharpness evaluation of colour images. Measurement Science and Technology. 27(2): 1–14.10.1088/0957-0233/27/2/025404Search in Google Scholar
18. Yi HA, Liu J, Ao P, Lu EH and Zhang H 2016 Visual method for measuring the roughness of a grinding piece based on color indices. Optics Express. 24(15): 17215–17233.10.1364/OE.24.017215Search in Google Scholar PubMed
19. Nammi S and Ramamoorthy B 2014 Effect of surface lay in the surface roughness evaluation using machine vision. Optik. 125: 3954–3960.10.1016/j.ijleo.2014.01.152Search in Google Scholar
20. Johnson RA and Wichern DW 2007 Applied Multivariate Statistical Analysis (6th ed.) (Beijing: Pearson Education Asia Ltd. and Tsinghua University Press).Search in Google Scholar
21. Hinton GE and Salakhutdinov RR 2006 Reducing the dimensionality of data with neural networks. Science. 313: 504–507.10.1126/science.1127647Search in Google Scholar PubMed
22. Zhang H, Liu J, Chen SF and Wang WF 2018 Novel roughness measurement for grinding surfaces using simulated data by transfer kernel learning. Applied Soft Computing. 73: 508–519.10.1016/j.asoc.2018.08.042Search in Google Scholar
23. Zhang H, Liu J, Lu EH, Suo XY and Chen N 2019 A novel surface roughness measurement method based on the red and green aliasing effect. Tribology International. 131: 579–590.10.1016/j.triboint.2018.11.013Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Verzeichnungskorrektur einer omnidirektionalen Kamera durch 3D-Referenzobjekte
- The influence of workpiece surface texture on visual measurement of roughness
- Deploying the high-power pulsed lasers in precision force metrology – Towards SI traceable and practical force quantization by photon momentum
- Quality analysis for condition monitoring of a manufacturing process integrating acoustic processing based on three distinct ML algorithms
- Three-dimensional finite element analysis and research of leakage magnetic field of flake, pit, through-hole defect in petroleum pipelines
- Generation of reproducible and scalable local damage to rolling bearings for the development of condition monitoring systems through the use of laser technology
Articles in the same Issue
- Frontmatter
- Research Articles
- Verzeichnungskorrektur einer omnidirektionalen Kamera durch 3D-Referenzobjekte
- The influence of workpiece surface texture on visual measurement of roughness
- Deploying the high-power pulsed lasers in precision force metrology – Towards SI traceable and practical force quantization by photon momentum
- Quality analysis for condition monitoring of a manufacturing process integrating acoustic processing based on three distinct ML algorithms
- Three-dimensional finite element analysis and research of leakage magnetic field of flake, pit, through-hole defect in petroleum pipelines
- Generation of reproducible and scalable local damage to rolling bearings for the development of condition monitoring systems through the use of laser technology
