The cutting process, which is a key processing technology in various industrial fields is achieving continuous growth, and the demand for high-quality cutting surfaces is continuously demanded. Plasma cutting continues to be studied for its excellent workability and productivity, but problems with cutting surface quality such as dross formation occur, so research to secure excellent cutting surface quality through appropriate control of process variables is essential. In this study, we propose a method for predicting surface roughness using real-time current and cutting speed data obtained while performing plasma cutting on A106 B steel pipe. Surface roughness was predicted based on the RBF algorithm applicable to prediction and control models. It was shown that the surface roughness of the plasma cutting surface can be predicted with the arc current waveform and process speed data. This study can be used as a basic study to control the surface roughness of the cut surface in real time.

Carbon steel pipes, which are essentially used in the manufacturing industry, are used in various fields due to the advancement of the industry, and a cutting process is essentially applied to pipes manufactured in a nominal size. The cutting process is the most basic and first process used to obtain a material in a desired shape, and it can affect the quality of subsequent processes such as welding or painting, so high-quality cutting surfaces are essential. Therefore, due to the advantage of improving productivity, it is essential to study to secure the appropriate quality of the cutting surface of the plasma cutting process, which is widely used in the industrial field. In this study, the effect of cut surface quality according to process parameters in the plasma cutting process for carbon steel pipe materials was analyzed. The surface roughness was measured to determine the quality of the cut surface, and the relationship between the surface roughness and the process variables was confirmed by selecting the arc current and cutting speed, which are identified as the main factors forming the surface roughness, as process variables.