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- Title
Milling Force Modeling Methods for Slot Milling Cutters.
- Authors
Wu, Mingzhou; Zhang, Guangpeng; Wang, Tianle; Wang, Rui
- Abstract
The slot milling cutter is primarily used for machining the tongue and groove of the steam turbine rotor, which is a critical operation in the manufacturing process of the steam turbine rotor. It is challenging to predict the milling force of a groove milling cutter due to variations in rake, rake angles and cutting speeds of the main cutting edge. Firstly, based on a limited amount of experimental data on turning, we have developed an equivalent turning force model that takes into account the impact of the rounded cutting edge radius, the tool's tip radius and the feed rate on tool's geometric angle. It provides a more accurate frontal angle for the identification method of the Johnson–Cook material constitutive equation. Secondly, the physical parameters, such as shear stress, shear strain and strain rate on the main shear plane, are calculated through the analysis of experimental data and application of the orthogonal cutting theory. Thirdly, the range of initial constitutive parameters of the material was determined through the split Hopkinson pressure bar (SHPB) test. The objective function was defined as the minimum error between the theoretical and experimental values. The optimal values of the Johnson–Cook constitutive equation parameters A, B, C, n and m are obtained through a global search using a genetic algorithm. Finally, the shear stress is determined by the governing equations of deformation, temperature and material. The axial force, torque and bending moment of each micro-segment are calculated and summed using the unit cutting force vector of each micro-segment. As a result, a milling force prediction model for slot milling cutters is established, and its validity is verified through experiments.
- Subjects
MILLING cutters; GENETIC algorithms; STEAM-turbines; BENDING moment; SHEARING force; CUTTING force; ANGLES; SHEAR strain; MILLING (Metalwork)
- Publication
Machines, 2023, Vol 11, Issue 10, p922
- ISSN
2075-1702
- Publication type
Article
- DOI
10.3390/machines11100922