Process Optimization for Laser Cladding Operation of Alloy Steel using Genetic Algorithm and Artificial Neural Network

This paper presents an investigation on single objective optimization for CO2 laser cladding process considering clad height (H) and clad width (W) as performance characteristics. This optimization of multiple quality characteristics has been done using Genetic Algorithm (GA) approach. The aim of this work is to predict the performance characteristics (H and W) at optimized condition by applying back propagation method of artificial neural network (ANN). The essential input process parameters are identified as laser power, scan speed of work table and powder feed rate. 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. It has also been found that the optimum condition of the cladding parameters for multi performance characteristics varies with the different combinations of weighting factors.