Modelling the corrosion behaviour of laser-deposited functionally gradient materials by experimentation and Bayesian regularisation algorithm

• Published in: NeuroQuantology Vol. 20; no. 10; p. 4730
• Main Authors: Yakkaluri Pratapa Reddy, Kavuluru, Lakshmi Narayana, Mantrala Kedar Mallik
• Format: Journal Article
• Language: English
• Published: Bornova Izmir NeuroQuantology 01.01.2022
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; Bayesian analysis; Chromium; Cobalt base alloys; Electrochemical analysis; Experimentation; Feed rate; Functionally gradient materials; Impedance; Laser deposition; Lasers; Low frequencies; Machine learning; Mathematical models; Molybdenum; Neural networks; Process parameters; Regularization; Surface roughness

This study employed a Bayesian Regulation-back propagation neural network (BR-BPNN) model to estimate the electrochemical behaviour of laser deposited functionally gradient material (FGM) samples made from SS-316L and Co-Cr-Mo alloy, where the samples are laser deposited using process parameters as...