Continuous Optimal Infeed Control for Cylindrical Plunge Grinding, Part 1: Methodology
Author:
Dong Shaoqiang1, Danai Kourosh1, Malkin Stephen1, Deshmukh Abhijit1
Affiliation:
1. Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, Amherst, MA
Abstract
A new methodology is developed for optimal infeed control of cylindrical plunge grinding cycles. Unlike conventional cycles having a few sequential stages with discrete infeed rates, the new methodology allows for continuous variation of the infeed rate to further reduce the cycle time. Distinctive characteristics of optimal grinding cycles with variable infeed rates were investigated by applying dynamic programming to a simulation of the grinding cycle. The simulated optimal cycles were found to consist of distinct segments with predominant constraints. This provided the basis for an optimal control policy whereby the infeed rate is determined according to the active constraint at each segment of the cycle. Accordingly, the controller is designed to identify the state of the cycle at each sampling instant from on-line measurements of power and size, and to then compute the infeed rate according to the optimal policy associated with that state. The optimization policy is described in this paper, and the controller design and its implementation are presented in the following paper [1].
Publisher
ASME International
Subject
Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering
Reference29 articles.
1. Dong, S., Danai, K., Malkin, S., and Deshmukh, A., 2003, “Continuous Optimal Infeed Control For Cylindrical Plunge Grinding-Part 2: Controller Design,” ASME J. Manuf. Sci. Eng., 125, pp. 334–340. 2. Malkin, S., 1989, Grinding Technology: Theory and Applications of Machining with Abrasives, Society of Manufacturing Engineers, Detroit, Michigan. 3. To¨nshoff, H. K., Zinngrede, M., and Kemmerling, M., 1986, “Optimization of Internal Grinding by Microcomputer-Based Force Control,” CIRP Ann., 35(1), pp. 293–296. 4. Jenkins, H. E., 1996, “Process Estimation and Adaptive Control of a Grinding System,” Ph.D. Thesis, Dept. of Mech. Eng., Georgia Institute of Technology, GA. 5. Brinksmeier, E.
, 1991, “Self Tuning Adaptive Control System for Grinding Process,” CIRP Ann., 40, pp. 355–358.
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