In this work, measurements and simulations were used to investigate the effects of grinding wheel topography on the geometric aspects of the grinding process. Since existing methods for measuring the grinding wheels were either not accurate enough or could only measure a small portion of a grinding wheel, a novel grinding wheel measurement system was developed. This system consists of a white light chromatic sensor, a custom designed positioning system and software. The resulting wheel scanning system was capable of measuring an entire grinding wheel with micron level accuracy. The system was used to investigate the effects of fine, medium and course dressing on grinding wheel surface topology and the resulting workpiece surface. New techniques were also developed to simulate metal removal in grinding. The simulation software consisted of a stochastic wheel model, dressing model and metal removal model. The resulting software could determine the uncut chip thickness, contact length for every cutting edge on a grinding wheel as well as the resulting surface roughness of the grinding wheel. The simulation was validated by comparing the wheel model used in the simulation to grinding wheel measurements and by comparing the simulated surface finish to the measured surface finish. There was excellent agreement between the predicted and experimentally measured surface topology of the workpiece. The results suggested that only 22 to 30% of the cutting edges exposed on the grinding wheel are active and that the average grinding chip is as much as 10 times thicker and 5 times shorter than would be produced by a grinding wheel with a regular arrangement of cutting edges as assumed by existing analytical approaches.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/21678 |
| Date | 01 April 2013 |
| Creators | Darafon, Abdalslam |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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