Function approximation and neural-fuzzy approach to machining process selection

The integration of neural networks and fuzzy logic provides an unique tool to improve the performance of solving ill-defined, nonlinear problems. In this paper, we first show a theoretical result that a class of fuzzy systems is a function approximator. This result extends Wang-Mendel´s work which is based on the Stone-Weierstrass theorem to a broader class of functions. Then we propose a neural-fuzzy technique for machining process selection (MPS), which usually is a crucial step in a semiconductor manufacturing environment and it constitutes a critical link between computer-aided design (CAD) and computer-aided manufacturing (CAM). Given the complexity of MPS process, a direct mathematical formulation and optimization to meet design specifications and cost constraints can be difficult or even formidable. By incorporating artificial neural networks learning and adaptation capability with fuzzy logic´s structured knowledge manipulation and reasoning, we are able to reduce the neural network training time and improve its prediction accuracy. Primary experimentation confirms the theoretical analysis and shows that the proposed technique is promising and has potential to be adopted in a real manufacturing environment