Concurrent design of manufacturing cell and control functions: A neural network approach

Abstract

This paper presents a neural network based decision support system (DSS) for use in concurrently determining cell configuration, operation plans, and complexity requirements of cell control functions. Advanced simulators and neural network technology are used in developing the DSS. Simulation experiments were conducted with many possible combinations of design changes to generate training pairs for a neural network. Complexity of cell control functions required by each design option was assessed, based on operational requirements, and was used to train another neural net. Once both neural networks are properly trained, one network can be used to predict the cell design configuration given a set of desirable cell performance measures, while the other network can be used to identify complexity requirements of the cell control functions by using the output provided by the first network as input to the second neural net. An operation-driven cell design methodology was applied to sequentially predict requirements of both cell configuration and cell control functions from the trained neural networks. This innovative new design methodology was illustrated via a successful implementation exercise in acquiring a real automated manufacturing cell at industrial settings. The exercise proves that such a DSS serves well as an effective tool for cell designers and the management in determining appropriate cell configuration and cell control functions at the design stage.