Solid particle erosion wear on plasma sprayed mild steel and copper surface. (2018)

• Record Type: Journal Article
• Authors: Swain, B.; Patnaik, A.; Bhuyan, S.K.; Barik, K.N.; Sethi, S.K.; Samal, S.; Mishra, S.C.; Behera, Ajit
• Is Part Of: Materials today. Volume 5:Number 9(2018)Part 3
• Page Start: 20403
• Page End: 20412
• Publication Date: 2018
• Subjects: mild steel -- copper -- erosive wear -- fly-ash -- quartz -- ilmenite; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2018.06.415
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Structural engineering components in plant made of mild steel and copper are affected by severe erosive wear due to presence of particulates in its surrounding. To protect these costly structural elements, we have taken atmospheric plasma spray process. This process is better for mild steel and copper due to several advance properties such as high temperature stability, coating efficiency, wear and corrosion protection. Coating by this process can be applied onto all suitable base materials with the widest variety of powders. In this investigation we have taken industrial waste like fly-ash, quartz and ilmenite powder as coating composite material and deposited on Mild Steel and Copper substrates with different weight proportions and different power levels of the plasma torch. Erosion type and their mechanism extensively investigated using scanning electron microscopy. Erosion properties have been studied using Air Jet erosion test Reg. with Silica erodent. Two significant parameters i.e. Erosion rate and Avg. microhardness have been measured by varying input i.e. power lever, velocity of erodent and time of erosion exposure. It is observed that, maximum erosion occurs at normal impact angle that indicates about brittle erosion condition. Different graphs are being plotted between mass loss-rate versus time period/impact pressure/impact angle, which gives good correlation with surface features observed. Based on these observations artificial neural network (ANN) models are developed to predict the result in various parameter setup.