Abstract:

It is a difficult task to analyze the performance of complex manufacturing systems like semiconductor assembly and testing systems. To fulfill the task, an efficient approach to develop a twomachine metalmodel based on simulation model as well as an approximate analytical method for large systems based on the developed metalmodel is proposed. A study of metamodeling of twomachine systems is carried out by using datadriven simulation technique in order to find out a fast and accurate method to build the metalmodel. Twomachine systems taking into account transfer delays in buffers are taken as a research object. To obtain cases for artificial neural network, secondary development based on ARENA is made to automatically configure and run simulation models and gather statistics. Three typical artificial neural networks for function approximation (BP, RBF and Chebyshev) are compared and optimized. The experiment results show that RBF model with 120 spread is the best. The low rate of error of that model indicates that it is accurate enough to be the building block of approximate analytical methods for the analysis of large systems.

Key words: data driven simulation, metamodel, artificial neural network