Abstract
This paper presents the development and experimental verification of a second-order polynomial regression model for predicting the adhesion strength of coatings produced by electric arc metallization (EAM). The aim of the study is to optimize three key process parameters: current strength (I), carrier gas pressure (P) and nozzle-to-substrate distance (L) in order to maximize the adhesion strength of the coating to the substrate. Experimental data were obtained from the central composite plan within the response surface method (RSM) and processed using analysis of variance (ANOVA). A pronounced synergistic interaction between pressure and distance was found (P × L), whereas current strength had no statistically significant effect in the range investigated. Optimal parameters (I = 200 A, P = 6.5 bar, L = 190 mm) provided an adhesion strength of ~15.4 kN, which was within 8.5% of the model’s prediction, confirming its accuracy. The proposed two-stage approach—combining statistical modeling with experimental fine-tuning in the global extremum zone—made it possible to improve the accuracy of the forecast and link statistical dependencies with the physical and mechanical mechanisms of adhesion formation (kinetic energy of particles, residual thermoelastic stresses). This method provides engineering-based recommendations for industrial application of EAM, reduces the cost of parameter selection, and improves the reproducibility of coating properties.