Modeling and throughput prediction for flexible parts feeders

Abstract

We illustrate a methodology for modeling and analyzing flexible feeders using generalized semi-Markov process (GSMP) models. Working through the simple case consisting of a single part being fed on a flexible feeder, we show how the throughput of the system may be obtained by both GSMP simulation and analytical techniques for GSMP models. Further, we demonstrate the predictive capability of such models. This is accomplished by generating and validating a model of the system feeding three distinct part types (at the same time) and then modifying the model to allow other feeding scenarios to be predicted. These scenarios include the effect of feeding the parts in a specific order, the effect of using a robot with different speed capabilities, and the effect of using a different-sized presentation conveyor. We validate the predictions with physical testing.