Modeling and Design of Experiments of Laser Cladding Process by Genetic Programming and Nondominated Sorting

Laser deposition of materials represents a modern additive technology that has a number of advantages over remaining technologies for depositing metallic materials. Besides a low-energy input, a quality bond, and minimal heat-affected zone, this technology is also characterized by the good mechanical properties of the deposited material that is a result of rapid cooling. Despite the prospects, this technology is still at the developing phase. New materials and techniques for determining optimal process parameters are being introduced. In this article, we developed a system for modeling (predicting) the properties of the deposited material and used design of experiments (DOE) for the laser cladding process parameter selection. Based on the experimental data obtained during cladding process, models were made for predicting the volume and roughness of the deposited material. Genetic programming was used for modeling the process. Then, a set of several thousand possible combinations (settings) of the machine parameters was produced on the basis of the obtained model. The most appropriate machine (process) parameters were selected in terms of deposition speed, powder efficiency, and surface roughness. These parameters were determined by nondominated sorting. The results offer the operator of the machine a set of appropriate process parameters that enable the production of high-quality products.