Grinding parameters optimisation of white marble using response surface methodology integrated with NSGA-II algorithm approach
by Fangchen Yin; Hongyuan Zhang; Changcai Cui; Guoqin Huang
International Journal of Abrasive Technology (IJAT), Vol. 11, No. 1, 2022

Abstract: This study combined response surface methodology (RSM), desirability function approach (DFA) and non-dominated sorting genetic algorithm (NSGA-II) to estimate the optimal grinding parameters that obtain the minimum surface roughness and contouring error value of white marble. A series of experiments was used to determine the effect of grinding parameters of robotic manipulators such as spindle speed, feed speed, grinding width and grinding depth on surface roughness (R_a) and contouring error (C_l). The R_a values of samples were measured by a stylus-type equipment and C_l values were obtained by employing a three-coordinate measuring machine. According to the results of the experimental design, the RSM was used to establish the prediction models of R_a and C_l. The parameters-optimised process that leads to minimum R_a and C_l was divided into three stages. Firstly, the established prediction model was optimised by DFA method. Secondly, the results obtained from DFA were chosen as the initial point of NSGA-II. Finally, the optimum grinding parameter values were obtained by using NSGA-II method. The experimental results showed that the R_a value of white marble reaches 3.166 μm, and the C_l value reaches 0.053 mm by the integrated optimisation approach.

Online publication date: Mon, 05-Sep-2022

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