This paper presents a dynamically compensated Spindle Integrated Force Sensor (SIFS) system to measure cutting forces. Piezo-electric force sensors are integrated into the stationary spindle housing. The structural dynamic model between the cutting forces acting on the tool tip and the measured forces at the spindle housing is identified. The system is first calibrated to compensate the influence of spindle run-out and unbalance at different speeds. Using the cutting force signals measured at the spindle housing, a Kalman Filter is designed to filter the influence of structural modes on the force measurements. The frequency bandwidth of the proposed sensor system is significantly increased with the proposed sensing and the signal processing method.
Skip Nav Destination
e-mail: spark@enme.ucalgary.ca
e-mail: altintas@mech.ubc.ca
Article navigation
September 2004
Technical Papers
Dynamic Compensation of Spindle Integrated Force Sensors With Kalman Filter
Simon S. Park,
e-mail: spark@enme.ucalgary.ca
Simon S. Park
Department of Mechanical Engineering, University of British Columbia, 2324 Main Mall, Vancouver, B.C., V6T 1Z4, Canada Phone: 604-822-2182, Fax: 604-822-2403
Search for other works by this author on:
Yusuf Altintas
e-mail: altintas@mech.ubc.ca
Yusuf Altintas
Department of Mechanical Engineering, University of British Columbia, 2324 Main Mall, Vancouver, B.C., V6T 1Z4, Canada Phone: 604-822-2182, Fax: 604-822-2403
Search for other works by this author on:
Simon S. Park
Department of Mechanical Engineering, University of British Columbia, 2324 Main Mall, Vancouver, B.C., V6T 1Z4, Canada Phone: 604-822-2182, Fax: 604-822-2403
e-mail: spark@enme.ucalgary.ca
Yusuf Altintas
Department of Mechanical Engineering, University of British Columbia, 2324 Main Mall, Vancouver, B.C., V6T 1Z4, Canada Phone: 604-822-2182, Fax: 604-822-2403
e-mail: altintas@mech.ubc.ca
Contributed by the Dynamic Systems, Measurement, and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received by the ASME Dynamic Systems and Control Division October 23, 2003. Associate Editor: E. A. Misawa.
J. Dyn. Sys., Meas., Control. Sep 2004, 126(3): 443-452 (10 pages)
Published Online: December 3, 2004
Article history
Received:
October 23, 2003
Online:
December 3, 2004
Citation
Park, S. S., and Altintas, Y. (December 3, 2004). "Dynamic Compensation of Spindle Integrated Force Sensors With Kalman Filter ." ASME. J. Dyn. Sys., Meas., Control. September 2004; 126(3): 443–452. https://doi.org/10.1115/1.1789531
Download citation file:
Get Email Alerts
Offline and online exergy-based strategies for hybrid electric vehicles
J. Dyn. Sys., Meas., Control
Optimal Control of a Roll-to-Roll Dry Transfer Process With Bounded Dynamics Convexification
J. Dyn. Sys., Meas., Control (May 2025)
In-Situ Calibration of Six-Axis Force/Torque Transducers on a Six-Legged Robot
J. Dyn. Sys., Meas., Control (May 2025)
Active Data-enabled Robot Learning of Elastic Workpiece Interactions
J. Dyn. Sys., Meas., Control
Related Articles
Identification of Spindle Integrated Force Sensor’s Transfer Function for Modular End Mills
J. Manuf. Sci. Eng (February,2006)
Miniaturized Cutting Tool With Triaxial Force Sensing Capabilities for Minimally Invasive Surgery
J. Med. Devices (September,2007)
Designing an Optical Bendloss Sensor for Clinical ForcMeasurement
J. Med. Devices (June,2009)
A Methodology to Measure Aerodynamic Forces on Cylinders in Channel Flow
J. Fluids Eng (August,2010)
Related Proceedings Papers
Related Chapters
Development of Intelligent Force Sensor System for Interactive Robot
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)
Cutting Performance and Wear Mechanism of Cutting Tool in Milling of High Strength Steel 34CrNiMo6
Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010)
Real-Time Prediction Using Kernel Methods and Data Assimilation
Intelligent Engineering Systems through Artificial Neural Networks