Region growth based segmentation to improve the porosity of Cu - (5–20%) W composite preforms
P. Radha, Mepco Schlenk Engineering College , Sivakasi
: Image segmentation, Region growing, composites, porosity analysis
While preparing the composite preform in the powder metallurgy Lab, the various defects due to porosity, open crack and residual stresses are possible. This may lead to poor life and strength of materials. It is difficult to predict the defects in the form of pores physically in the powder metallurgy Lab. To simplify this kind of problem, the Scanning Electron Microscope (SEM) images are generated from the powder composites and are segmented using region growth approach to find the distribution of pores. Normally, the composite preforms are being produced through various processes like mechanical milling, mixing, compaction, sintering and hot extrusion. In this study, Cu–(5–20%) W composite preforms, with a preform density of 94% are prepared. The pore size in term of coverage area, perimeter during different sintering atmospheres are derived. Further, the porosity is reduced during extrusion process. The results of SEM images are compared before and after sintering and extrusion process. This kind of work will aid the manufacturing process of material parts in predicting their strength and life time.
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[1] Rafael C.Conzalez, Richard E.Woods, Steven L.Eddins, “Digital Image processing”, Pearson Education, 2008.
[2] N. Leema, P.Radha, S.C. Vettivel, H. Khanna Nehemiah, “Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network”, Materials and Design 68 (2015) pp.195–206.
[3] Shwetabh Singh, “Microscopic Image Analysis of Nanoparticles by Edge Detection using Ant Colony Optimization”, IOSR Journal of Computer Engineering 11(3) (2013) pp. 84-89.
[4] Azmi Tawfik Alrawi , Saad Jasim Mohammed, “Determination The Porosity of CdS Thin Film by Seed Filling Algorithm”, International Journal on Soft Computing 3(3) (2012) pp:1-12
[5] Meena Sundari P.,Meenambal T.,and P. D. “Arumairaj, Artificial Neural Networks based prediction of removal of basic dye from its aqueous solution using activated carbon”, Proceedings of the 3rd World Conference on Applied Sciences, Engineering and Technology 27-29, Kathmandu, Nepal, ISBN 13: 978-81-930222-0-7, (2014) pp 313-317.
[6] Gary Chinga, “Structural studies of LWC paper coating layers using SEM and image analysis Techniques”, Ph.D. thesis, Norwegian University of Science and Technology, February 2002.
[7] Manuel F.M.Costa, “Application of image processing to the characterization of nano structures”, Advanced Material Science 6 (2004) pp.12-20.
[8] M. G. Cortina-Januchs, J. Quintanilla-Dominguez,, A. Vega-Corona, A. M. Tarquis,D. Andina, “Detection of pore space in CT soil images using artificial neural networks”, Biogeo sciences, 8, (2011) pp.279–288.
[9] Aref Naimzad, Yousef Hojjat, Mojtaba Ghodsi, “Comparative Study on Mechanical and Magnetic Properties of Porous and Nonporous Film-shaped Magneto rheological Nanocomposites Based on Silicone Rubber” International Journal of Innovative Science and Modern Engineering, 2(8) (2014) pp:11-20.
[10] Ahmet H. Aydilek, A.M.ASCE; Seyfullah H. Oguz,, and Tuncer B. Edil, M.ASCE ,” Digital Image Analysis to Determine Pore Opening Size Distribution of Nonwoven Geotextiles:, Journal of computing in civil engineering, Vol:16,issue:4, (2002) pp: 280-290.
[11] Shawn Zhang, Robert E. Klimentidis, Patrick Barthelemy, “Study of membrane morphology of SEM image of polymer nanocomposite membrane by digital Image processing”, International Symposium of the Society of Core Analysts held in Austin, Texas, USA, 2011.
Paper ID: GRDJEV01I120049
Published in: Volume : 1, Issue : 12
Publication Date: 2016-12-01
Page(s): 148 - 155
Published in: Volume : 1, Issue : 12
Publication Date: 2016-12-01
Page(s): 148 - 155