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Nonparametric Tool Path Compensation for Machining Flexible Parts

[+] Author Affiliations
Waku Hatakeyama

University of Tokyo, Tokyo, Japan

Cong Wang, Lu Lu

New Jersey Institute of Technology, Newark, NJ

Paper No. DSCC2016-9640, pp. V002T18A001; 7 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5070-1
  • Copyright © 2016 by ASME


This paper discusses the compensation of tool paths for machining flexible parts. Despite various research published on the topic, machining in practice nowadays remains limited to tool path planning based on only the geometric models of the parts and tools. This is mainly because that tool path compensation methods usually require accurate physical information of the systems and rely on analytical or finite element simulations, which are often not available to the end-users. In regards to this problem, this paper presents data-oriented nonparametric learning methods that require solely the geometric measurements of the trial machined contour(s). The physical parameters of the parts and tools as well as simulations of the machining process are not required. Two algorithms are developed based on Gaussian Process Regression and Artificial Neural Network respectively. Experimental tests are conducted. A plan of further improving the results using an auxiliary real-time vision sensor is also discussed.

Copyright © 2016 by ASME
Topics: Machining



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