Modeling and evaluation of workpiece accuracy error mapping for ultra-precision machine tools based on virtual machining

Abstract

To address the challenges of multi-source error mapping and evaluation in ultra-precision turning, a modeling and evaluation method utilizing virtual machining is proposed. First, a spatial kinematic model of the machine tool incorporating geometric errors is constructed using Multi-Body System (MBS) theory. Subsequently, virtual point clouds are generated via three-dimensional Cartesian deviation direct projection. Coupled with this, a robust reconstruction algorithm for the mechanical axis is developed by integrating hierarchical spatial filtering and Principal Component Analysis (PCA). Simulation results demonstrate that under the specified source errors, both the eccentricity and tilt errors of the workpiece are quantified at the sub-micron level. Furthermore, this study reveals the "error self-absorption effect" inherent in the turning process and systematically analyzes the perturbation of the spindle's dynamic synchronous runout on centering precision. This research provides a solid theoretical foundation for the forward precision design of machine tools.