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Analysis of the effecting parameters on laser cutting process by using response surface methodology (RSM) method

Hasan H. K.

Journal of Achievements in Materials and Manufacturing Engineering

2022

Vol. 110, nr 2

59--66

Purpose: The main aim of this research is to analyse the effects of the laser cutting parameters on the kerf width of stainless steel (2505), and develop a model of laser cutting that can predict the relation between the characteristics of the resultant kerf width and the process input parameters. Design/methodology/approach: To achieve the minimum kerf width; the optimal setting of the effecting parameters like; power supply, cutting speed, and gas pressure for the response surface methodology, and factorial design-based optimal parametric analysis has been carried out for this purpose. A mathematical model for analysis of the kerf width was developed using the (Minitab 16) on the basis of experimental results. Findings: It’s found that the interaction between power value, cutting speed, and pressure has a significant effect on the response value. Also, it’s found that, when the power and cutting speed are set at optimal values i.e. 1250 watt and 5 mm/min, the minimum kerf width will be 0.389 mm. The mathematical model has been established based on regression analysis by factorial design and response surface model. Research limitations/implications: The cutting quality in this process widely depend on the technical specifications of a laser machine. Consequently, machine operation parameters are considered the main limitations factor in this process. Practical implications: In this current work, the 4 mm thickness stainless steel (2505) has been used in the experimental investigation to measure the Influence of laser cutting machine parameters. In addition, optimal laser cutting parameter values that minimize the width of kerf width were identified. The optimization problem was formulated and solved by the second-order model method. The laser cutting experiment was planned and conducted according to the (RSM) central composite design approach (uncode). Originality/value: The validation with experimental results shows that the factorial analysis gives an average error 5%, and the (R-sq) is equal to 69.02%. It’s concluded that the model that has the (R-sq) value greater than 41% is considered as a fit model and can be used for the next machining process.