Subsystem modelling using neural networks with application to manufacturing systems

Where traditional methods of robust design have their limitations in manufacturing systems, it appears that system modelling can provide advantages. When manufacturing systems can be split into subsystems, subsystem models and continuity and compatibility constraints are required to develop an overall system model. Where physical information is lacking, empirical models can be created. Herein, the radial basis function neural network method for empirical modelling is discussed and procedures for connecting these models with physical system information is demonstrated using a cooling fin problem