Milling Force Estimation Based on Spindle Model

Date

2024-11

Author

Altınel, Batuhan

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This thesis proposes a real-time estimation method for milling cutting forces, using intrinsic signals such as motor currents, voltages, and angular encoder feedback. The approach relies on a spindle model with electrical and mechanical subsystems, where the electrical subsystem includes an AC induction motor and the mechanical subsystem is represented as a two-inertia system. Unknown motor parameters are identified through traditional methods, while frequency response tests determine unknown mechanical subsystem parameters. The Extended Kalman Filter serves as an observer, estimating forces by integrating system dynamics with geometry-based tool-workpiece interaction data. Five distinct estimation methods are developed. The first method uses the entire spindle model; the second addresses asynchronous measurements; the third employs a reduced model focusing only on the mechanical subsystem; the fourth accommodates cases with unknown cutting geometry; and the fifth compensates for measurement drift, enhancing robustness over time. The methods are implemented and validated on a Deckel CNC milling machine setup. Tests are conducted under various process parameters, with tools of differing diameters and teeth counts, to evaluate performance and robustness. Results demonstrate the effectiveness and adaptability of these estimation methods for practical cutting operations, underscoring the proposed approach’s suitability for real-time applications in industrial settings.

Subject Keywords

Real-Time Estimation, CNC Machining, Milling Force, Force Estimation, Kalman Filter

URI

https://hdl.handle.net/11511/112401

Collections

Graduate School of Natural and Applied Sciences, Thesis