Neural Network Predictive Process Models: Three Diverse Manufacturing Applications | S. Y. Lam Sarah et al Predictive Process Models Three Diverse Manufacturing Applications Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton CRC Press LLC 2001 11 Neural Network Predictive Process Models Three Diverse Manufacturing Applications Sarah S. Y. Lam State University of New York at Binghamton 113 Alice E. Smith Auburn University Introduction to Neural Network Predictive Process Models Ceramic Slip Casting Application Abrasive Flow Machining Application Chemical Oxidation Application Concluding Remarks Introduction to Neural Network Predictive Process Models In a broad sense predictive models describe the functional relationship between input and output variables of a data set. When dealing with real-world manufacturing applications it is usually not an easy task to precisely define the set of input variables that potentially affect the output variables for a particular process. Oftentimes this is further complicated by the existence of interactions between the variables. Even if these variables can be identified finding an analytical expression of the relationship may not always be possible. The process of selecting the analytical expression and estimating the parameters of the selected expression could be very time-consuming. Neural networks a field that was introduced approximately 50 years ago have been getting more attention over the past 15 years. There are a number of survey papers that summarize some of the applications of neural networks. Udo 1992 surveys within the manufacturing domain which covers resource allocation scheduling process control robotic control and quality control. Zhang and Huang 1995 provide a good overview of many manufacturing applications. Hussain 1999 discusses a variety of applications in chemical process control. One of the advantages of neural network modeling is its ability to learn relationships through the data itself rather than assuming the functional form
