The regression analysis process is used to study and predicate the surface response by using the design of experiment (DOE) as well as roughness calculation through developing a mathematical model. In this study; response surface methodology and the particular solution technique are used. Design of experiment used a series of the structured statistical analytic approach to investigate the relationship between some parameters and their responses. Surface roughness is one of the important parameters which play an important role. Also, its found that the cutting speed can result in small effects on surface roughness. This work is focusing on all considerations to make interaction between the parameters (position of influence) because laser power directly depends on cutting speed with high gas pressure and vice versa to obtain a less surface roughness. Data analysis showed that the lower value for roughness was (0.68) µm and the high roughness was (8.56) µm. The model values for considered fit are suggested to be as (R2) = 51.76%. The selected coefficient is referred to the amount of model variation in the response. The adjusted value of (R2) = 8.34% will calculate the number of forecasting model and is normally useful for comparing models with different numbers of predictions. The (F) test is used to determine whether interaction and major effects are significant. The p-value is the probability of obtaining a statistic test which is considered as the extreme of actual calculated values if the null hypothesis is true. A commonly cut-off value that used for the p-value is (0.05). This value is used to be the active contribution to investigate the correlation between laser machining parameter with the surface roughness. Also the goal of this research highlights the experimental control parameters of mild steel laser processing with their responses.
