TY - GEN
T1 - Nonparametric tool path compensation for machining flexible parts
AU - Hatakeyama, Waku
AU - Wang, Cong
AU - Lu, Lu
N1 - Publisher Copyright:
Copyright © 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85015713151&partnerID=8YFLogxK
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U2 - 10.1115/DSCC2016-9640
DO - 10.1115/DSCC2016-9640
M3 - Conference contribution
AN - SCOPUS:85015713151
T3 - ASME 2016 Dynamic Systems and Control Conference, DSCC 2016
BT - 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
PB - American Society of Mechanical Engineers
T2 - ASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Y2 - 12 October 2016 through 14 October 2016
ER -