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Vision Based Surface Roughness Characterization of Flat Surfaces Machined with EDM
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 11, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
Surface roughness measurement is an important one in any manufacturing next to dimensions. In this investigation, a vision system and image processing tools were used to develop reliable surface roughness characterization technique for Electrical Discharge Machined surfaces. A CMOS camera with red LED light source were used for capturing images of EDMed surfaces. A separate signal vector generated for all the images from its image pixel intensity matrices. The mean, skewness and kurtosis were obtained from the signal vector. The mean, skewness and kurtosis of the images signal vector correlates very well with the stylus measured hybrid roughness parameters Rda and Rdq. Hence the technique may be preferred for online surface roughness characterization of Electrical Discharge Machined (EDMed) surfaces.
- Mahashar Ali - BSA Crescent Institute of Science & Technology
- Siddhi Jailani - BSA Crescent Institute of Science & Technology
- Murugan Mariappan - Vellore Institute of Technology
- Mangalnath Anandan - BSA Crescent Institute of Science & Technology
- Vignesh Pavithran - BSA Crescent Institute of Science & Technology
CitationAli, M., Jailani, S., Mariappan, M., Anandan, M. et al., "Vision Based Surface Roughness Characterization of Flat Surfaces Machined with EDM," SAE Technical Paper 2019-28-0148, 2019, https://doi.org/10.4271/2019-28-0148.
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