Precision surface topography prediction in ball-end milling: a CWER-based approach for enhanced accuracy and efficiency
摘要
Surface topography plays a critical role in the performance and durability of machined parts, making its accurate prediction essential for optimizing machining processes and achieving high-quality finishes. This paper introduces a novel predictive model for surface topography in ball-end milling, based on the Z-MAP principle and an innovative integration of the Cutter Workpiece Engagement Region (CWER). Unlike conventional approaches, which often rely on global calculations, our method focuses on the CWER, enhancing prediction accuracy, particularly in the bottom contact area between the tool and the workpiece. By restricting computations to the engagement zone, the model increases discretization precision while significantly reducing computational time, overcoming a major limitation of traditional methods. An advanced algorithm implementing this methodology is developed to account for tool runout, trajectory, machining parameters, and inclination angles. The model is validated experimentally and through simulations covering a broad range of machining conditions, demonstrating its robustness and adaptability. Results highlight the critical influence of machining parameters on surface topography: increasing the feed per tooth fz leads to a rise in the maximum height parameter Sz, with surface quality deteriorating at higher feed rates. At elevated radial depth of cut ae, its effect on Sz becomes dominant, reducing the influence of fz. Furthermore, optimal tool inclination significantly improves surface quality by minimizing Sz. The integration of the CWER algorithm not only enhances engagement angle predictions but also reduces computational time by 50%, making it a highly efficient tool for optimizing surface finish in ball-end milling applications.