Machining accuracy reliability during the peripheral milling process of thin-walled components

During the peripheral milling process of thin-walled components, a workpiece with poor rigidity will cause workpiece deformation error because of the milling force and result in the degradation of machining accuracy and related machining ability of machine tools. As a result, how to obtain the workpiece deformation error and its effect on the surface machining quality of workpiece is the focus of the research. Hence, a synthesis approach was developed in this study to analyze the machining accuracy reliability during the peripheral milling process of thin-walled components. The feedback mechanism between the milling force and milling deformation error was studied, and then a workpiece deformation error model based on an advanced neural fuzzy network was developed. By applying the D-H method, a machining accuracy model of the machine tool was established for the machine tool considering the workpiece deformation. Based on the reliability analysis method combined with RF and Edge worth, a machining accuracy reliability model was developed, and then the reliability of machining accuracy during the peripheral milling process of thin-walled components was obtained. To verify this approach, a machining experiment was conducted on a three-axis machine tool; the experimental results indicate that better predictive ability was achieved using the approach presented in the paper.