Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters

Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control

“…The patterns identified then serve as the primary information for identifying the causes of unnatural process behaviour. Reports of using neural networks for pattern recognition can be found in Hwarng and Hubele (1993a,b), Hwarng and Chong (1995), Cheng (1995Cheng ( , 1997, Chang and Aw (1996), Cook and Chiu (1998), Guh and Hsieh (1999), Guh and Tannock (1999), Chang and Ho (1999), Perry et al (2001), Cook et al (2001).…”

Using Minimum Quantization Error chart for the monitoring of process states in multivariate manufacturing processes

“…Cook and Chiu (1998) developed Radial Basis Function (RBF) networks for detecting mean shifts in correlated processes. Cook and Zobel (2001) proposed BPNs to detect variance shifts in correlated processes. Hwarng (2004) used regression BPNs scheme to monitor process mean shifts in correlated processes.…”

Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

“…With the rapid development of computer technology, many artificial intelligence algorithms in machine learning, such as neural networks (NN), support vector machines (SVM) and decision trees, etc., have found broad application to multivariate process control. Cook et al (2001) and Low et al (2003) apply back propagation (BP) to construct monitoring scheme for detecting variance shifts in multivariate process. Wang and Chen (2002) propose a neural-fuzzy model based on BP to detect mean shifts and classify shift magnitudes.…”