Friction and wear behaviour prediction of HVOF coatings and electroplated hard chromium using neural computation

The present study was to investigate and to compare the friction behaviour of High-Velocity Oxy-Fuel (HVOF) sprayed Tribaloy(C)-400, Cr3C2-25%NiCr, WC-12%Co coatings, and electrodeposited hard chromium (EHC), using a robust implicit formulation for a possible replacement of EHC in gas turbine shaft repair. The formalism is based on artificial intelligence and is implemented to discover the correlations between the wear test parameters and the friction coefficient. Such correlations are represented by an Artificial Neural Network (ANN) trained with the aid of experimental sets organized in a database. An ANN optimization procedure permitted to predict the wear behavior for intermediate conditions is not present in the experimental sets. Based on the predicted results, some conclusions were drawn pointing out the wear behavior of each considered material and the benefit of using the implicit formulation. (C) 2003 Elsevier B.V. All rights reserved.