Neural Network Modeling of Abrasive Flow Machining

This paper discusses the preliminary development of a neural network-based process monitor and off-line controller for abrasive flow machining of automotive engine intake manifolds. The process is only observable indirectly, yet the time at which machining achieves the specified air flow rate must be estimated accurately. A neural network model is used to estimate when the process has achieved air flow specification so that machining can be terminated. This model uses surrogate process parameters as inputs because of the inaccessibility of the product parameter of interest, air flow rate through the manifold during processing. The primary project participants are Extrude Hone, Ford Motor Company and the University of Pittsburgh.