This work presents a systems approach in machining process control. Traditional force-based machining process control has been focused on single machine-single operation. The force or power sensor is used to measure the instantaneous force/power, and control action is taken by changing the feedrate in real time to follow a given force setpoint. The application of such control has successfully been implemented to prevent chatter and to elongate tool life by minimizing tool wear. This research seeks to extend the application of control algorithms to learn about the machining system (comprised in this context of a workpiece being operated on in progressive machining), and how knowledge generated by the process can be passed on to the next process for optimization. To demonstrate this, turning of a partially hardened bar is explored. A nonlinear mechanistic force model-based control framework attempts to control the cutting force at a designated setpoint, with material properties changing over the cut. The force coefficients for the material are calculated offline using experimental data and Bayesian inference methods. Since the hardened part of the bar will shift the force coefficient values, an online estimation strategy (Bayesian Recursive Least Square estimator) is used to learn the new coefficients as well as satisfying the control objective. With the newly learned coefficients passed downstream, the subsequent operation experiences no compromise of control objective as well reduces the maximum values of force encountered. Numerical analyses presented show the adaptation and control scheme performance.
ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference
June 10–14, 2013
Madison, Wisconsin, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-5546-1
PROCEEDINGS PAPER
Model Learning in a Multistage Machining Process: Online Identification of Force Coefficients and Model Use in the Manufacturing Enterprise
Parikshit Mehta
,
Parikshit Mehta
Clemson University, Clemson, SC
Search for other works by this author on:
Laine Mears
Laine Mears
Clemson University, Greenville, SC
Search for other works by this author on:
Author Information
Parikshit Mehta
Clemson University, Clemson, SC
Laine Mears
Clemson University, Greenville, SC
Paper No:
MSEC2013-1144, V002T02A026; 8 pages
Published Online:
November 27, 2013
Citation
Mehta, Parikshit, and Mears, Laine. "Model Learning in a Multistage Machining Process: Online Identification of Force Coefficients and Model Use in the Manufacturing Enterprise." Proceedings of the ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. Volume 2: Systems; Micro and Nano Technologies; Sustainable Manufacturing. Madison, Wisconsin, USA. June 10–14, 2013. V002T02A026. ASME. https://doi.org/10.1115/MSEC2013-1144
Download citation file:
- Ris (Zotero)
- Reference Manager
- EasyBib
- Bookends
- Mendeley
- Papers
- EndNote
- RefWorks
- BibTex
- ProCite
- Medlars
Close
Sign In
Related Proceedings Papers
Related Articles
Machining Process Monitoring and Control: The State-of-the-Art
J. Manuf. Sci. Eng (May, 2004)
Modular CNC Design for Intelligent Machining, Part 2: Modular Integration of Sensor Based Milling Process Monitoring and Control Tasks
J. Manuf. Sci. Eng (November, 1996)
The Effect of Preheating of Work Material on Chatter During End Milling of Medium Carbon Steel Performed on a Vertical Machining Center (VMC)
J. Manuf. Sci. Eng (November, 2003)
Related Chapters
Design and Implementation of a Low Power Java CPU for IC Bank Card
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
The Genesis of Tool Wear in Machining
Advances in Multidisciplinary Engineering
Study of the Effect of Machining Parameters on Material Removal Rate and Electrode Wear during Electric Discharge Machining of Mild Steel
International Conference on Computer and Automation Engineering, 4th (ICCAE 2012)