Simulation-Assisted Prediction of Surface Topography for Milling Strategy Selection in Freeform 3-Axis and 5-Axis Ball-End Milling

Abstract

Surface topography plays a critical role in the functional performance of machined freeform components, yet its assessment traditionally takes place only after manufacturing. This work presents a simulation-assisted framework for predicting the surface topography generated during freeform ball-end milling and using these predictions to support machining-strategy selection. Three finishing strategies (3-axis square, SQ3, 3-axis spiral, SP3, and 5-axis spiral, SP5) were investigated on a representative freeform benchmark by combining high-resolution UVRMAP simulations with experimental topography measurements. Surface signatures were characterized using ISO 25178 areal parameters together with two- and one-dimensional Fast Fourier Transform (FFT) analyses, enabling quantitative evaluation of surface amplitude, preferential orientations, anisotropy, and characteristic spatial periodicities. The results demonstrate that machining strategy and local surface geometry produce distinctive topographical signatures that cannot be fully described by conventional roughness parameters alone.Theproposed UVRMAP methodology accurately reproduces the primary spatial organization of the geometric texture generated by the programmed toolpath, while the incorporation of a controlled non-ideal cutter-edge representation improves the prediction of experimentally observed fine-scale features. The combined experimental–simulation methodology provides a robust framework for comparing and validating the spatial and morphological characteristics of predicted surface topographies, providing a basis for future machining-strategy selection according to application-specific surface requirements.