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2023 | Vol. 68, iss. 4 | 1333--1347
Tytuł artykułu

Preparation and Characterization of Stainless Steel-Molybdenum Composite Coatings and its Evaluation using Image Processing

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The objective of this paper is to develop a Non Destructive Testing (NDT) method for the detection and classification of defects in composite materials at a micro level and to devise methodologies to analyse the corrosion resistance behavior using Scanned Electron Microscope (SEM) imagery. The defects on the Stainless Steel - Molybdenum (SS-Mo) Nanocomposite coating is estimated from their Scanning Electron Micrographs by using Image Processing algorithms. For this, the SS-Mo Nano Composite coatings are fabricated using a DC magnetron sputtering process using an indigenously prepared sputtering target. Depositions are carried out on Glass substrate for the evaluation of structural, morphological, chemical composition and corrosion resistance of the coatings prepared under different conditions (deposition of SS at 300°C and RT (Room Temperature); deposition of SS + Mo at 300°C and RT). The structural and compositional analysis performed with X-ray Diffraction (XRD) and Energy-Dispersive X-ray spectroscopy (EDX) has confirmed the formation of Stainless Steel Molybdenum Composite, when the deposition is at 300°C. The SS-Mo composite deposited at 300°C is also observed to yield high corrosion resistance of the order 0.058 mm/year. A novel texture – morphology based image feature descriptor has been proposed for corrosion resistance to evaluate the composite material in a Non-destructive manner. The analysis of SEM image of the developed coatings using the proposed feature along with machine learning algorithm reveals the superior property for SS-Mo coatings deposited at 300°C which is also demonstrated by the laboratory experiments.
Wydawca

Rocznik
Strony
1333--1347
Opis fizyczny
Bibliogr. 20 poz., fot., rys., tab., wzory
Twórcy
autor
  • Thiagarajar College of Engineering, Mechanical Engineering, India
  • Thiagarajar College of Engineering, Electronics and Communication Engineering, India
  • Thiagarajar College of Engineering, Chemistry, India
Bibliografia
  • [1] S. Hanish Anand, T. Subash Murugan, S. Shanmuga Pandian, Fabrication and Mechanical Testing Of Mmc Formed by Reinforcement of Alumina & Graphite in Aluminium LM 24, International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) 21, 3 (2016).
  • [2] G.R. Raghav, N. Selvakumar, K. Jeyasubramanian, M.R. Thansekhar, Corrosion analysis of copper - TiO2 nanocomposite coatings on steel using sputtering, International Journal of Innovative Research in Science, Engineering and Technology 3, 3 (2014),
  • [3] Liu Rong, Jianhua Yao, Qunli Zhang, Matthew, X. Yao, Rachel Collier, Effects of molybdenum content on the wear/erosion and corrosion performance of low-carbon Stellite alloys, Materials & Design 78, 95-106 (2015).
  • [4] M.C. Thirumoolam, B. Sivaramakrishnan, M. Devarajan, Sputtering deposition of aluminium molybdenum alloy thin film anodes for thin film microbatteries, Electronic Materials Letters 11, 3, 416-423 (2015).
  • [5] I.B. Singh, M. Singh, S. Das, A comparative corrosion behawior of Mg, AZ31 and AZ91 alloys in 3.5% NaCl solution, Journal of Magnesium and Alloys 3, 2, 142-148 (2015).
  • [6] S. Zhang, S. Wang, C.L. Wu, C.H. Zhang, M. Guan, J.Z. Tan, Cavitation erosion and erosion-corrosion resistance of austenitic stainless steel by plasma transferred arc welding, Engineering Failure Analysis 76, 115-124 (2017).
  • [7] E. Jafari, Corrosion behaviors of two types of commercial stainless steel after plastic deformation, Journal of Materials Science & Technology 26, 9, 833-838 (2010).
  • [8] T.M. Chandran, S. Balaji, Sputtering Deposition of Sn-Mo-based Composite Anode for Thin-Film Li-Ion Batteries, Journal of Electronic Materials 45, 6, 3220-3226 (2016).
  • [9] M.S. Priyan, P. Hariharan, Wear and Corrosion Resistance of Fe Based Coatings by HVOF Sprayed on Gray Cast-Iron for Automotive Application, Tribology in Industry 36, 4 (2014).
  • [10] I. Sulima, P. Putyra, P. Hyjek, T. Tokarski, Effect of SPS parameters on densification and properties of steel matrix composites, Advanced Powder Technology 26, 4, 1152-1161 (2015).
  • [11] D. Dwivedi, K. Lepková, T. Becker, Carbon steel corrosion: a review of key surface properties and characterization methods, RSC Advances 7, 8, 4580-4610 (2017).
  • [12] N.A. Otsu, Threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics 9, 1, 62-6 (1979).
  • [13] S. Sridhar, ‘Digital Image Processing’, Oxford University Press (2011).
  • [14] Chaplot Sandeep, L.M. Patnaik, N.R. Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomedical Signal Processing and Control 1, 1, 86-92 (2006).
  • [15] M.C. Pravin, S. Karthikeyan, B. Sathyabama, D.S. Vinothini, Texture and morphology based conductivity analysis of fuel cell-bipolar plate using scanning electron microscopic images, Indian Journal of Engineering & Materials Sciences 24, 261-269 (2017).
  • [16] M. Şenel, M. Gürbüz, Investigation on Mechanical Properties and Microstructures of Aluminum Hybrid Composites Reinforced with Al2O3/GNPs Binary Particles, Archives of Metallurgy and Materials 66 (2021).
  • [17] S. Manigandan, T.R Praveenkumar, A.M. Al-Mohaimeed, K. Brindhadevi, A. Pugazhendhi, Characterization of polyurethane coating on high performance concrete reinforced with chemically treated ananas erectifolius fiber, Progress in Organic Coatings 150, 105977 (2021).
  • [18] G. Pitchamuthu, M. Sekar, A. Anderson, D, Jayakumar, Evaluation of iron-epoxy metal nanocomposite in glass fibre and Kevlar, International Journal of Ambient Energy 39 (2), 122-126 (2018).
  • [19] R.C. De Amorim, B. Mirkin, Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering, Pattern Recognition 45 (3), 1061-1075 (2012).
  • [20] K. Uma, B. Sathya Bama, D. Sabarinathan, Identification and retrieval of medicinal plants from Southern India using Efficient-B4Net, Journal of Intelligent & Fuzzy Systems, (Preprint), 1-16. (2022).
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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