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Vision Based Surface Roughness Characterization of Flat Surfaces Machined with EDM
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.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Rahman, M., Khan, M., Kadirgama, K., Noor, M., and Bakar, R. , “Experimental Investigation into Electrical Discharge Machining of Stainless Steel 304,” Journal of Applied Sciences 11(3):549-554, 2011.
- Khan, M.A.R., Rahman, M., and Rahman, M.S. , “Optimal Set-Up and Surface Finish Characteristics in Electrical Discharge Machining on Ti-5Al-2.5 Sn Using Graphite,” Perspectives in Science 8:440-443, 2016.
- Habib, S.S. , “Study of the Parameters in Electrical Discharge Machining through Response Surface Methodology Approach,” Applied Mathematical Modelling 33(12):4397-4407, 2009.
- Keskin, Y., Halkacı, H.S., and Kizil, M. , “An Experimental Study for Determination of the Effects of Machining Parameters on Surface Roughness in Electrical Discharge Machining (EDM),” The International Journal of Advanced Manufacturing Technology 28(11-12):1118-1121, 2006.
- Mascaraque-Ramirez, C. and Franco, P. , “Experimental Study of Surface Finish During Electro-Discharge Machining of Stainless Steel,” Procedia Engineering 132:679-685, 2015.
- Chen, X., Wang, Y., and Wang, Z. , “Investigations on Material Removal Rate and Surface Roughness of Meso Gear by Micro Wire-EDM,” International Journal of Manufacturing Research 13(2):134-150, 2018.
- Mohapatra, K.D., Dash, R., and Sahoo, S.K. , “Analysis of Process Parameters in Wire Electric Discharge Machining of Gear Cutting Process Using Entropy Grey Relational Analysis Approach,” International Journal of Manufacturing Research 12(4):423-443, 2017.
- Santos, I., Polli, M.L., and Daniel, H. , “Influence of Input Parameters on the Electrical Discharge Machining of Titanium Alloy (TI-6AL-4V),” International Journal of Manufacturing Research 10(3):286-298, 2015.
- Ali, J.M. and Murugan, M. , “Surface Roughness Characterisation of Turned Surfaces Using Image Processing,” International Journal of Machining and Machinability of Materials 19(4):394-406, 2017, doi:10.1504/ijmmm.2017.086166.
- Suhail, S.M., Ali, J.M., Jailani, H.S., Murugan, M. , “Vision Based System for Surface Roughness Characterisation of Milled Surfaces Using Speckle Line Images,” In: IOP Conference Series: Materials Science and Engineering, 2018. 1. IOP Publishing, 012054
- Ali, J.M., Jailani, H.S., Murugan, M. (2019), “Surface Roughness Evaluation of Milled Surfaces by Image Processing of Speckle and White-Light Images,” In: Advances in Manufacturing Processes. Springer, 141-151
- Shivanna, D., Kiran, M., and Kavitha, S. , “Evaluation of 3D Surface Roughness Parameters of EDM Components Using Vision System,” Procedia Materials Science 5:2132-2141, 2014.
- Kumar, R., Kulashekar, P., Dhanasekar, B., and Ramamoorthy, B. , “Application of Digital Image Magnification for Surface Roughness Evaluation Using Machine Vision,” International Journal of Machine Tools and Manufacture 45(2):228-234, 2005.
- Cuka, B., Cho, M., and Kim, D.-W. , “Vision-Based Surface Roughness Evaluation System for End Milling,” International Journal of Computer Integrated Manufacturing 31(8):727-738, 2018.
- Alkoot, F.M. , “A Review on Advances in Iris Recognition Methods,” International Journal of Computer Engineering Research 3(1):1-9, 2012.
- Boles, W.W. and Boashash, B. , “A Human Identification Technique Using Images of the Iris and Wavelet Transform,” IEEE Transactions on Signal Processing 46(4):1185-1188, 1998.
- Daugman, J. (2009), “How Iris Recognition Works,” In: The Essential Guide to Image Processing. Elsevier, 715-739
- Daugman, J.G. , “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11):1148-1161, 1993.
- de Martin-Roche, D., Sanchez-Avila, C., Sanchez-Reillo, R. , “Iris Recognition for Biometric Identification Using Dyadic Wavelet Transform Zero-Crossing,” In: Security Technology, 2001, in IEEE 35th International Carnahan Conference on, 2001. IEEE, 272-277
- Sanchez-Avila, C., Sanchez-Reillo, R., de Martin-Roche, D. (2002), “Iris-Based Biometric Recognition Using Dyadic Wavelet Transform,” IEEE Aerospace and Electronic Systems Magazine 17 (10):3-6
- Tang, X., Xiao, H., Ding, H., and Liu, J. , “Surface Roughness Measurement Based on Image Processing and Image Recognition,” Computers and Simulation in Modern Science 91-96, 2009.
- Jeyapoovan, T. and Murugan, M. , “Surface Roughness Classification Using Image Processing,” Measurement 46(7):2065-2072, 2013.