Application of artificial neural network for the prediction of laser cladding process characteristics at Taguchi-based optimized condition

Abstract

This paper presents an investigation on the optimization of multiple performance characteristics during CO2 laser cladding process considering clad width and clad depth as performance characteristics. This optimization for multiple quality characteristics has been done using Taguchi’s quality loss function. The process model for laser cladding operation using various techniques like artificial neural network (ANN) has rarely been found in the literature review. In the present work, a number of experiments have been performed to establish the interrelationship between process variables and response variables using the back propagation method of ANN. The essential input process parameters are identified as laser power, scan speed of work table, and powder feed rate. Moreover, the analysis of variance is also employed to determine the contribution of each control parameter on clad bead quality. In order to validate the predicted result, an experiment as confirmatory test is carried out at the optimized cladding condition. It is observed that the confirmatory experimental result is showing a good agreement with the predicted one. However, it has been found that the optimum condition of the cladding parameters for multi-performance characteristics varies with the different combinations of weighting factors.