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Autonomous system for cylindrical plunge grinding
Abstract
An autonomous system has been developed for cylindrical plunge grinding to optimize the operating parameters while taking complete sets of part quality and machine related constraints into account. The system is capable of adjusting the operating parameters from part to part to minimize cycle time while satisfying part quality and machine related constraints in response to in-process and post-process measurements which characterize the processing conditions and part quality. Two important constraint models for out-of-roundness and taper have been developed analytically and verified experimentally. Existing process and constraint models have also been improved and verified experimentally. Two optimization strategies have been developed to find closed-form solutions for real-time control to minimize the grinding time and also to minimize the production time which includes optimization of the dressing interval. The system is capable of coping with the quantitative uncertainty of the process by using a newly developed predictive model for the uncertain parameters and employing parameter estimation to update the models from part to part. Modified strategies for accelerated control have also been developed to reduce transient times. Optimization strategies were first evaluated in simulation. Practical implementation and testing of the autonomous system was then performed on an internal grinder, retrofitted with electrical drives and sensors and interfaced to a personal computer for data acquisition, system identification, and machine control. The system has also been successfully applied on a production machine in industry. The results of this investigation provide the scientific and technological basis for commercial development of a new generation of grinding systems and for retrofitting of older grinders.
Subject Area
Mechanical engineering|Industrial engineering
Recommended Citation
Xiao, Guoxian, "Autonomous system for cylindrical plunge grinding" (1996). Doctoral Dissertations Available from Proquest. AAI9619457.
https://scholarworks.umass.edu/dissertations/AAI9619457