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This paper provides Petri net (PN) modeling and performance analysis of a surface mount device (SMD) electronics manufacturing assembly line for an automated remanufacturing of printed circuit boards. Concentrating on the operational aspects, PN models for an automated assembly stations were constructed. These models enable designers to have a better understanding of the system control and analysis from the graphical representations of PNs. In this context, the selection of the particular buffer size and its effects on the production rate of the transferline are explored. PN models are designed to analyze two different transferlines and to find out when local gains propagate to the end of the transferline. Furthermore, artificial neural networks (ANN) are proposed as a fast function approximation tool for a rapid re-analysis of the remanufacturing system. ANN can easily 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.       Key words: Electronics remanufacturing, stochastic Petri nets, artificial neural networks, surface mount device, performance analysis.

Productivity analysis of an electronics re-manufacturing system through stochastic Petri nets and artificial neural networks