Surface Flaw Detection of Plug Valve Material Using Infrared Thermography and Weighted Local Variation Pixel-Based Fuzzy Clustering Technique. (8th August 2022)

• Record Type: Journal Article
• Authors: Jacintha, V.; Karthikeyan, S.; Sivaprakasam, P.
• Other Names: Kavimani Vijayananth Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2022(2022)
• Publication Date: 2022-08-08
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2022/7919532
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : This study focuses on the identification and categorization of plug valve defects. We utilize a thermal fluke camera to obtain the plug valve thermal images. The thermal camera utilizes passive infrared thermography towards the identification of plug valve defects such as cracks, porosity, and internal defects. These flaws depict variation in surface temperature induced by heat flux. Infrared thermography is capable of identification of surface flaws such as cracks and subsurface flaws such as porosity. Its flaw identification range is effective only up to a certain depth in the metal. The heat flux variations are clearly visible for surface cracks and subsurface porosity. However, the heat flux shows no fluctuations for internal defects. Hence, to identify the internal defects in the metal, we opt for a combination of passive infrared thermography and dye penetrating test. In the dye penetrating test, a thinned paint is applied over the metal surface that aids in the identification of cracks, porosity, and internal defects as well. The PIT-DPT (passive infrared thermography-dye penetrating test) works in combination with weighted local variation pixel-based fuzzy clustering (WLVPBFC) to measure the depth of the defects. The defects were measured against parametric quantities such as F-value, precision, recall, accuracy, Jaccard index, TP, FP, TN, FN, FP rate, TP rate, and MCC. These parameters depict variations with regard to surface texture and extent of defect level. The PIT-DPT and WLVPBFC techniques identify metal flaws with 87.88% efficiency when evaluated against other existing algorithms.