ANN SUPPORTED STUDY ON THE PERFORMANCE AND SLURRY EROSION RESISTANCE OF THERMAL SPRAYED WC20Cr3C2 7Ni COATINGS

Digvijay G. Bhosale, Poonam Bhosale, Amrut Bhosale, Yogesh Ingale, Hitesh Vasudev and T. Ram Prabhu
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Digvijay G. Bhosale: Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Pune, Maharashtra 411 018, India
Poonam Bhosale: ��Department of Artificial Intelligence and Data Science, D. Y. Patil College of Engineering, Pune 411 044, India
Amrut Bhosale: ��Department of Mechatronics Engineering, Rajarambapu Institute of Technology, Rajaramnagar, Shivaji University, Kolhapur, Maharashtra 415 414, India
Yogesh Ingale: �Department of Mechanical Engineering, Veermata Jijabai Technological Institute, Matunga, Mumbai, Maharashtra 400 019, India
Hitesh Vasudev: �School of Mechanical Engineering, Lovely Professional University, Phagwara 144 411, India
T. Ram Prabhu: ��CEMILAC, Defence R and D Organization, DRDO, Bangalore, Karnataka 560 093, India

Surface Review and Letters (SRL), 2025, vol. 32, issue 06, 1-12

Abstract: The thermal spray coatings are commonly employed in slurry pump components and hydrodynamic turbine blades, where wear progression is an intricate phenomenon. In this research work, the performance analysis of HVOF and APS sprayed WC20Cr3C27Ni coatings for slurry erosion wear is carried out by using artificial neural networks (ANN). The influence of time, particle size, impact angle, speed, and slurry concentration on wear performance of coatings and turbine steel substrate are evaluated. Under the experimental settings, slurry erosion wear rates and mass loss for both coatings and substrate were determined. When ASTM A743 steel was coated with thermal sprayed WC20Cr3C27Ni coatings, the slurry erosion wear resistance of the steel was enhanced by 2 and 3.5 times for APS and HVOF coatings, respectively. The design of ANN made it possible to examine the interactions between the seven input variables. A robust model was formed by the two outputs that followed. This model enables the prediction of slurry erosion wear rate and mass loss of WC20Cr3C27Ni coatings and substrate.

Keywords: Artificial neural network; thermal sprayed coatings; slurry erosion (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218625X24020013

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