Title: Prediction of erosive wear in AISI 304 stainless steel coated with Stellite 6 powder using adaptive neuro-fuzzy inference system

Authors: Sangita Sarangi; Ajit Kumar Mishra; Seshadev Sahoo; Kamalakanta Muduli

Addresses: Department of Mechanical Engineering SOA (Deemed to be University), Institute of Technical Education and Research (of Affiliation), Bhubaneswar, India ' Department of Mechanical Engineering SOA (Deemed to be University), Institute of Technical Education and Research (of Affiliation), Bhubaneswar, India ' Department of Mechanical Engineering SOA (Deemed to be University), Institute of Technical Education and Research (of Affiliation), Bhubaneswar, India ' Department of Mechanical Engineering, Papua New Guinea University of Technology, Lae, Morobe, Papua New Guinea

Abstract: An attempt is made to deposit cobalt-based Stellite 6 powder with uniform spherical shapes and diameters ranging from 15 µm to 45 µm on an AISI 304 rolled stainless steel substrate without any bond coat. Test specimens with the requisite dimensions per ASTM standard were cut for erosion testing in a tribotester with alumina as the erodent. Experiments were carried out at room temperature with a normal impingement angle, and erosive wear was observed. To validate the resulting models, the experimental erosion volume loss as output from experiments in an erosion tribotester is compared to that of the theoretical and constructed ANFIS models. The investigations were carried out depending on coating thickness, utilising three specimens with thicknesses of 74 µm, 128 µm and 215 µm, respectively. When the theoretical analysis was compared to the experimental and anticipated ANFIS models, close tolerances of 6.48%, 0.836%, and 6.04% deviance were detected for specimen-1.

Keywords: erosion; wear; Stellite 6; coating; ANFIS.

DOI: 10.1504/IJPMB.2024.135754

International Journal of Process Management and Benchmarking, 2024 Vol.16 No.2, pp.164 - 179

Received: 14 Sep 2022
Accepted: 04 Oct 2022
Published online: 04 Jan 2024 *

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