Modeling and performance analysis of an SMD assembly station using stochastic Petri nets and artificial neural networks

Abstract

The present paper provides Petri net (PN) modeling and performance analysis of a surface mount device (SMD) assembly station for automated manufacturing of printed circuit boards. Concentrating on the operational aspects, the authors construct PN models for an automated assembly station. These models enable designers to have a better understanding of the system control and analysis from the graphical representations of PNs. Further, the selection of the particular buffer size and its effects on the production rate of the transferline are explored. The analysis mainly uses the PN model to analyze two different transferlines and to examine when local gains propagate to the end of the transferline. Furthermore, artificial neural networks (ANN) are also proposed as a fast function approximation tool for a rapid reanalysis of the system. ANN can predict the output of the transferline for unknown input patterns when the input and output relation is monotonically increasing or decreasing. This capability of the ANN proves to be useful to analyze the transferline when there is no further information available. The approaches as presented in this paper can be generalized and applied to many other applications of multi-robot assembly systems.