Surface Roughness Measurement Using Light Sectioning Method and Computer Vision Techniques.

Authors

N. Elhamshary, Workers University, Mansoura, Egypt
Ossama Badie Abouelatta, Production Engineering and Mechanical Design Department,El- Mansoura University, Faculty of Engineering, Mansoura, EgyptFollow
Ibrahim Mohamed Amar, Production Engineering and Mechanical Design Department., El- Mansoura University, Faculty of Engineering, Mansoura, Egypt
Elamir Gadelmawla, Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, 35516 Mansoura, EgyptFollow

Corresponding Author

Amar, Ibrahim Mohamed

Subject Area

Production Engineering and Mechanical Design

Article Type

Original Study

Abstract

The development of new industries has led to a requirement for super-smooth surfaces and for the ability to measure surfaces of industrial parts accurately; therefore, the measurement of engineering surface roughness is becoming increasingly important. In this work, a new approach is introduced to measure surface roughness by combining a light sectioning microscope and a computer vision system. This method has the advantage of being non-contact. The light sectioning microscope is used to view roughness profiles of the specimens to be measured and the vision system is used to capture images for the viewed profiles. A special program (named SRLSVision) has been totally developed in-house using MatlabTM software to analyze the captured images through two modules. The first module is used to extract the roughness profiles from the captured images by applying various image processing and computer vision algorithms. The second module is used to calculate the ISO roughness parameters (22 parameters) from the extracted profiles. The system has been calibrated for metric units and verified using a standard specimen. In addition, the system was used to measure various samples: machined by different operations and the obtained results were compared with the results obtained by measuring the same samples using a stylus instrument. The accuracy of the system proved to be within ±5.5% compared with the stylus instrument readings.

Recommended Citation

Elhamshary, N.; Abouelatta, Ossama Badie; Amar, Ibrahim Mohamed; and Gadelmawla, Elamir (2020) "Surface Roughness Measurement Using Light Sectioning Method and Computer Vision Techniques.," Mansoura Engineering Journal: Vol. 29 : Iss. 1 , Article 20.
Available at: https://doi.org/10.21608/bfemu.2020.132820