Modelling of process parameters in laser polishing of steel components using ensembles of regression trees

Laser polishing of steel components is an emergent process in the automation of finishing operations in the industry. The aim of this work is to develop a soft computing tool for surface roughness prediction of laser polished components. The laser polishing process depends primarily on three factors: surface material, initial topography and energy density. Although the first two factors can be reasonably estimated, the third one is often unknown under real industrial conditions. The modelling tool developed solves this limitation. The application is composed of four stages: a data-acquisition system, a data set generated from the inputs, a soft computing model trained and validated with the data set. Finally, the model obtained is used to generate different plots of industrial interest. Different prediction models are tested until the most accurate one is selected, in order to generate the soft computing model, and due to the highly complex phenomena that influence surface roughness generation in laser polishing. Ensembles of regression trees yield the best results for the methods under consideration (multilayer perceptrons, radial basis function networks and support vector machines). It has been proven that the results of an ensemble, which is a combination of several models, are better than single methods in many applications.