## 4 Discussion
### Quadratic Predictive Model
** & \(-\)**95.00\%** & **95.00\%** \\ \hline Mean/Intercept & 0.348 & 0.011 & 31.012 & 0.000 & 0.322 & 0.374 & 0.348 & 0.011 & 0.322 & 0.374 \\ \hline (1)Power [W](L) & \(-\)0.022 & 0.027 & \(-\)0.808 & 0.442 & \(-\)0.086 & 0.041 & \(-\)0.011 & 0.014 & \(-\)0.043 & 0.021 \\ \hline Power [W](Q) & \(-\)0.022 & 0.024 & \(-\)0.933 & 0.378 & \(-\)0.077 & 0.033 & \(-\)0.011 & 0.012 & \(-\)0.039 & 0.016 \\ \hline (2)Pressure [bar](L) & \(-\)0.033 & 0.032 & \(-\)1.050 & 0.324 & \(-\)0.107 & 0.040 & \(-\)0.017 & 0.016 & \(-\)0.053 & 0.020 \\ \hline Pressure [bar](Q) & \(-\)0.022 & 0.027 & \(-\)0.808 & 0.442 & \(-\)0.086 & 0.041 & \(-\)0.011 & 0.014 & \(-\the process and the influence of the independent parameters. The normal distribution of the cutting width is within a maximum of two standard deviations from the central mean (media): cnf. limit (-95%) = \(\mu\)-2\(\sigma\) = 0.322 mm and cnf. limit (+95%) = \(\mu\) + 2\(\sigma\) = 0.374 mm. The Student's \(t\)-test (t) equal to 31.01196 and the probability \(p\) < 0.05 (corresponding to the average, on the first line of the table) show that the relationship between the influencing factors and the cutting width is appreciable. The value distribution of the cutting width is normal, the distribution graph is plotted by the Gaussian curve, and the maximum height is the arithmetic mean \(\mu\) = 0.348 mm. For verification, the initial experiment was replicated four times, at equal intervals. The average cutting width for each replica was calculated. The following values were obtained: \(\mu_{1}\) = 0.335 mm, \(\mu_{2}\) = 0.338 mm, \(\mu_{3}\) = 0.317 mm, \(\mu_{4}\) = 0.348 mm. Notice that replica 3 has the best average. For the 3 rd replica we have: \(\sum(X_{i}-\mu_{3})^{2}\) = 0.045883, standard deviation: \(\sigma_{3}\)= 0.0420, and \(t_{\rm calc}\) = \(\frac{\mu_{3}-1}{\sqrt{n}}\) = \(-\)3.875, \(t_{\rm crit}\) = 1.706, critical intervals \((-\infty,-t_{crit}]\cup[t_{crit},+\infty)\), probability \(p\) = 0.95, significance level \(1-p\) = 0.05, number of the block n = 27, number of freedom degrees d\(t\) = n - 1 = 26. The \(t\)-test parameter of the Student distribution belongs to the critical region, and the H\({}_{0}\) hypothesis is rejected, so that the average cutting width of the initial design differs significantly from the average of three replicates. In conclusion, there is a statistically significant difference between the two averages. In the case of reply 4, an uncontrolled disturbing factor intervened, which altered to some extent the experimental results. Table 7 uses the significance of the indices: (1) laser power, (2) gas pressure, (3) cutting speed. Q and L also refer to the term square and linear. 1L by 2Q means the influence of linear power combined with square gas pressure. The effect determines the correlation coefficient of the shear parameters in determining the polynomial that describes the Kerf answer. The parameter of the Student's \(t\)-test distribution belongs to the critical region, and the H\({}_{0}\) hypothesis is rejected, so that the average cutting width of the initial design differs significantly from the average of three replicates. In conclusion, there is a statistically significant difference between the two averages. In the case of reply 4, an uncontrolled disturbing factor intervened, which altered to some extent the experimental results. Table 8 uses the significance of the indices (1) laser power, (2) gas pressure, (3) cutting speed. In addition, Q and L refer to the square and linear term. 1L by 2Q means the influence of linear power combined with square gas pressure. The effect determines the correlation coefficient of the cutting parameters in determining the polynomial that describes the Kerf answer. The developed regression models have acceptable values of standard deviation. Due to the high standard error, the probability of error is high. The probability \(p\) > 0.05 shows that the terms of the model are not significant (Table 8).