Total views : 97
Optical Coherent based Metal Surface Roughness Detection using Radial Basis Function
Objective: In the manufacturing industry, the surface roughness places a vital role to make a quality product. The surface roughness is measured by line contact and non-contact methods till now. In this paper, we propose a non-contact method of surface roughness measurement using the optical coherent method. Methods/Statistical Analysis: In the optical coherent method, a radial basis algorithm is used. So far surface roughness is evaluated for an overall region of the material. The standard specimen image was taken using web camera and processed, analysed and compared with the images of the specimen to be tested using MATLAB software. Findings: By using a radial basis algorithm, we found that micro irregularities surfaces in the material are detected, and depth of penetration of lights over the material will detect the micro irregularities and measure the surface roughness automatically. Application/Improvements: Here the images were taken using web camera having Complementary Metal Oxide Semiconductor (CMOS) sensor that can be replaced by taking images using a high resolution Charged Coupled Devices (CCD) camera to improve the accuracy of results. Now a days, smart cameras are available in the market, hence the speed of the processing the images can be achieved.
Machine Vision; MATLAB Software; PIC Microcontroller; Pneumatic Control System
- Tikhe C, Chitode JS. Metal surface inspection for defect detection and classification using gabor filter. International Journal of Innovative Research in Science, Engineering and Technology. 2014 Jun; 3(6):13702–9.
- Sarosi Z, Knapp W, Kunz A, Wegener K. Detection of surface defects on sheet metal parts using one-shot deflectometry in the infrared range, IWF, ETH Zurich, Switzerland, FLIR Technical series; 2010. p. 1–10.
- Sharifzade M, Alirezaee S, Sadri AR. Detection of Steel Defect Using the Image Processing Algorithms. Proceedings of the 12th IEEE International Multitopic Conference, 2008 Dec 23-24.
- Gonzalez G, Feunte, Miguel, Peran. On-line machine-vision system to detect split defects in sheet metal forming processes.Proceedings of the 18th International Conference on Pattern Recognition
- ; USA. 2006. p. 723–6.
- Nizam MSH, Marizan S, Zaki SA, Zamzuri ARM. Vision based identification and classification of weld defects in welding environments: A review. Indian Journals of Science and Technology. 2016; 9(20):1–15.
- Jaffery NA, Dubey AK. Scope and prospects of non-invasive visual inspection systems for industrial applications. Indian Journal of Science and Technology. 2016 Jan; 9(4):1–11.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.