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Milling Force Estimation Based on Spindle Model
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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
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B. Altınel, “Milling Force Estimation Based on Spindle Model,” M.S. - Master of Science, Middle East Technical University, 2024.