Application of experimental-based modeling to laser cladding

Abstract

This article addresses experimental-based modeling of the powder injection laser cladding process. An accurate model for the process is essential for controller design and process automation. There are few dynamic and static models for the process, however, due to the limitations and intensive numerical calculations, their application to real-time process control are not practical. Two model structures, the Hammerstein–Wiener model and Elman recurrent neural network are implemented to identify the laser cladding dynamic model. The type of experiments and collected data are explained, and model predictions for different and unseen data are compared with experimental results. It is shown that the Hammerstein–Wiener approach can more accurately describe the transient response of the process. Results are promising and show that experimental-based models can effectively be used in laser cladding processes.