Abstract
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.
Highlights
In the last few years, the number of rework stations available on the electronics manufacturing market has grown considerably including automated ones, but there has still been no significant reduction in the number of defects
Significant improvement in automated rework has been made by the authors, it has been shown the outcome of the automated rework line has not produced a high enough reliable yield percentage (Fidan et al, 2004; Fidan, 2004; Fidan et al, 2006). The objective of this investigation is to make a contribution towards this surface mount electronics remanufacturing process by analyzing their performance via stochastic Petri nets and artificial neural networks, so that the defects that necessitate a rework operation could be analyzed to predict the production rate of the transferline, Figure 1
The relations between three independent variables which are N1,N2 and N3 buffer sizes were chosen as the input parameters and overall production rate P was chosen as an output parameter
Summary
In the last few years, the number of rework stations available on the electronics manufacturing market has grown considerably including automated ones, but there has still been no significant reduction in the number of defects. Significant improvement in automated rework has been made by the authors, it has been shown the outcome of the automated rework line has not produced a high enough reliable yield percentage (Fidan et al, 2004; Fidan, 2004; Fidan et al, 2006) The objective of this investigation is to make a contribution towards this surface mount electronics remanufacturing process by analyzing their performance via stochastic Petri nets and artificial neural networks, so that the defects that necessitate a rework operation could be analyzed to predict the production rate of the transferline, by using prior information instead of performing reanalysis of the whole system. PNs can be used to study the problem of optimal operational settings by concurrent assembly systems and to evaluate the production rates of various possible models
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