A hybrid neural fuzzy system is proposed to monitor both process mean and variance shifts simultaneously. | Chang Shing I A Hybrid Neural Fuzzy System for Statistical Process Control Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton CRC Press LLC 2001 18 A Hybrid Neural Fuzzy System for Statistical Process Control Shing I Chang Kansas State University Statistical Process Control Neural Network Control Charts A Hybrid Neural Fuzzy Control Chart Design Operations and Guidelines for Using the Proposed Hybrid Neural Fuzzy Control Chart Properties of the Proposed Hybrid Neural Fuzzy Control Chart Final Remarks Abstract A hybrid neural fuzzy system is proposed to monitor both process mean and variance shifts simultaneously. One of the major components of the proposed system is composed of several feedforward neural networks that are trained off-line via simulation data. Fuzzy sets are also used to provide decision-making capability on uncertain neural network output. The hybrid control chart provides an alternative to traditional statistical process control SPC methods. In addition it is superior in that 1 it outperforms other SPC charts in most situations in terms of faster detection and more accurate diagnosis and 2 it can be used in automatic production processes with minimal human intervention a feature the other methods ignore. In this chapter theoretical base operations user guidelines chart properties and examples are provided to assist those who seek an automatic SPC strategy. Statistical Process Control Statistical process control SPC is one of the most often applied quality improvement tools in today s manufacturing as well as service industries. Instead of inspecting end products or services SPC focuses on processes that produce products and services. The philosophy of a successful SPC application is to identify sources of special causes of production variation as soon as possible during production rather than wait until the very end. Here production is defined as either a manufacturing or .
