Optimization of plasma-powder surfacing parameters using regression-correlation analysis

Abstract

The article presents a methodology for optimizing the parameters of plasma-powder surfacing using regression-correlation analysis. Plasma-powder surfacing is a highly efficient method of applying wear-resistant coatings, which is widely used in mechanical engineering, metallurgy, shipbuilding, and ship repair. The surfacing process depends on a number of parameters, such as arc current and voltage, surfacing speed, and plasma torch angle. These factors significantly affect the performance characteristics of the resulting coatings. In this work, the influence of technological parameters of plasma-powder surfacing on the strength and durability of deposited materials was investigated, and a mathematical model was built to optimize the technological process. A full factorial experiment was used for four variables at two levels, which allowed us to evaluate the interaction between the parameters and their influence on the endurance limit of the samples. The results show that the deposition current, arc voltage, and plasma torch angle have the greatest influence on the quality of coatings, while the deposition speed is a less significant factor. The built regression model provides accurate forecasting with a determination coefficient of R² = 0.9365, which indicates a high correspondence of the model to real data. The developed methodology reduces the consumption of materials and time, improves the quality of coatings and increases the efficiency of the plasma-powder surfacing process. The practical application of the results involves the use of a mathematical model to adjust the deposition parameters depending on the requirements for the final product. This opens up new opportunities for automation and productivity improvement in industrial environments.