In the case that study of interaction between changing variables is of vital importance, a second-order regression function gives a deeper understanding. This methodology provides too few unique design points compared to a full factorial design, therefore the quadratic function is well-known as the most popular class of RSM for fit- ting the experimental data. Despite the fact that cubic regression functions have rarely been applied to experimental data, when the responding variable is very sensitive to changing variables, the interaction between parameters is deemed to be of vital impor- tance; a third-order model provides a broader understanding of process nature. This can be observed from the following equation: