eprintid: 15750
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/57/50
datestamp: 2023-11-10 03:30:22
lastmod: 2023-11-10 03:30:22
status_changed: 2023-11-10 02:00:18
type: article
metadata_visibility: show
creators_name: Shaik, N.B.
creators_name: Mantrala, K.M.
creators_name: Narayana, K.L.
title: Prediction of corrosion properties of LENSTM deposited cobalt, chromium and molybdenum alloy using artificial neural networks
ispublished: pub
keywords: Biocompatibility; Chromium alloys; Cobalt alloys; Corrosive effects; Marine applications; Marine engineering; Molybdenum alloys; Seawater corrosion, Artificial neural network modeling; Cobalt-chrome-molybdenum alloys; Corrosion property; Engineering applications; Laser engineered net shaping; Metallic component; Potentiodynamics; Process parameters, Neural networks
note: cited By 1
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. Copyright © 2021 Inderscience Enterprises Ltd.
date: 2021
publisher: Inderscience Publishers
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106877575&doi=10.1504%2fIJMPT.2021.115212&partnerID=40&md5=a4333e2876037bcc06ce4da731822d49
id_number: 10.1504/IJMPT.2021.115212
full_text_status: none
publication: International Journal of Materials and Product Technology
volume: 62
number: 1-3
pagerange: 152-166
refereed: TRUE
issn: 02681900
citation:   Shaik, N.B. and Mantrala, K.M. and Narayana, K.L.  (2021) Prediction of corrosion properties of LENSTM deposited cobalt, chromium and molybdenum alloy using artificial neural networks.  International Journal of Materials and Product Technology, 62 (1-3).  pp. 152-166.  ISSN 02681900