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Prediction and Optimization of a Ceramic Casting Process Using A Hierarchical Hybrid System of Neural Networks and Fuzzy Logic (1999)
| Citations: | 6 - 0 self |
BibTeX
@MISC{Lam99predictionand,
author = {Sarah S. Y. Lam and Kimberly L. Petri and Alice E. Smith},
title = {Prediction and Optimization of a Ceramic Casting Process Using A Hierarchical Hybrid System of Neural Networks and Fuzzy Logic},
year = {1999}
}
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Abstract
This paper is a case study that describes a hybrid system integrating fuzzy logic, neural networks and algorithmic optimization for use in the ceramics industry. A prediction module estimates two quality metrics of slip-cast pieces through the simultaneous execution of two neural networks. A process improvement algorithm optimizes controllable process settings using the neural network prediction module in the objective function. An expert system module contains a hierarchy of two fuzzy logic rule bases. The rule bases prescribe processing times customized to individual production lines given ambient conditions, mold characteristics and the neural network predictions. This paper demonstrates the applicability of newer computational techniques to a very traditional manufacturing process and the system has been implemented at a major U.S. plant.
Keyphrases
neural network fuzzy logic hierarchical hybrid system ceramic casting process algorithmic optimization mold characteristic prediction module objective function expert system module fuzzy logic rule base case study ambient condition quality metric neural network prediction ceramic industry hybrid system simultaneous execution traditional manufacturing process neural network prediction module slip-cast piece process improvement algorithm individual production line computational technique controllable process setting major u.s. plant
