Abstract
ABSTRACT Nowadays, computational and computing tools are widely used in prediction applications. Neural computing is a modern and emerging technique to predict data efficiently and precisely. In this context, erosion investigation of Mo2C–WC10Co4Cr high-velocity oxy-fuel (HVOF) deposited on AISI 316L was carried out in the present work by implementing a neural computing and Taguchi’s method. This research paper focuses on neural network (NN) technique for the prediction of erosion in Mo2C–WC10Co4Cr HVOF coating. The WC10Co4Cr powder was composed of 3% (by weight) of molybdenum carbide, each with a concentration of 3 wt.%. The input parameters used for designing the NN model were erodent properties (bulk density, circularity factor, average particle size, and slurry concentration), material properties (hardness and porosity of bare/coated), and process parameters (speed and time). A set of experiments was optimised by using Taguchi’s method (L16 2 × 5 array). Results indicated that the overall accuracy of Pearson coefficient (R) was found as 0.99582 with experimental data. However, R = 1 was found for testing results.
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