Title: Prediction of corrosion properties of LENSTM deposited cobalt, chromium and molybdenum alloy using artificial neural networks

Authors: Nagoor Basha Shaik; Kedar Mallik Mantrala; Kavuluru Lakshmi Narayana

Addresses: Department of Mechanical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia ' Department of Mechanical Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, A.P., India ' Department of Mechanical Engineering, KL Education Foundation (Deemed University), Guntur, A.P., India

Abstract: The corrosion properties of a material play an essential role in the life of metallic components, especially in biomedical and marine engineering applications. Cobalt-chrome-molybdenum alloy, a well-known biocompatible material, has been tested for its potentiodynamic properties. The samples are fabricated with laser engineered net shaping (LENSTM). Potentiodynamic polarisation tests are performed by scanning the samples at a rate of 2 mVs-1. The artificial neural network model has been developed for the prediction of the properties, as mentioned above, using the experimental data sets. The results of the model are found to be satisfactory as the overall R squared value is 0.9982. The developed model helps in estimating the potentiodynamic properties of the LENS deposited cobalt, chromium, and molybdenum materials with the process parameters that have not experimented, and it saves the experimental process time for various purposes.

Keywords: corrosion; Co-Cr-Mo alloy; laser engineered net shaping; artificial neural networks; prediction; additive manufacturing; potentiodynamic properties; process parameters; training; testing; and validation.

DOI: 10.1504/IJMPT.2021.115212

International Journal of Materials and Product Technology, 2021 Vol.62 No.1/2/3, pp.4 - 15

Received: 05 Feb 2020
Accepted: 02 Oct 2020

Published online: 24 May 2021 *

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