Neural Networks, Design of Experiments and Other Optimization Methodologies to Quantify Parameter Dependence of Atmospheric Plasma Spraying

Off-line control of thermal spraying generally implements the quantification of the energetic, environmental, injection and geometric conditions. The interdependence of these parameters with regard to the deposit properties were studied with different statistical methodologies. The aim was to point out the large possibilities of the Artificial Neural Network methodology in the process optimization. In that way, experiments were designed considering atmospheric plasma spraying and the following operating parameters: arc current intensity, powder feed rate, carrier gas flow rate, total plasma gas flow rate, primary hydrogen content and scanning step. These parameters were related to the coating porosity level and hardness (HK). For each case, simple correlations were studied with none linear regressions, design of experiments and a Multilayer Perceptrons (MLP, with two hidden layers). Non linear correlations were compared with MLP based on the Average Sum of Absolute Error (ASAE) and other correlation factors (RSS, R², Ra², STD, RS and AR).