An Adaptive Statistically Based Controller for Through-Feed Centerless Grinding

Abstract

The purpose of this research is to develop a statistically based controller that is “self-tuning.” High volume manufacturing processes such as through-feed centerless grinding are best controlled with a statistical approach, but traditional methods of statistical control generally rely on fixed parameters that must be determined. These values must be precisely known and the true physical characteristics they model must remain constant throughout grinding, or traditional statistical control methods may break down. The mean and standard deviation of a process are measures of its accuracy and precision. The scheme developed here makes control decisions based on the real-time values of these quantities. This self-adjusting ability can compensate for changes in machine parameters as they occur.