Back propagation artificial neural networks for the analysis of quality control charts

Velasco, Tomas; Rowe, Mark R.

doi:10.1016/0360-8352(93)90305-h

Cited by 25 publications

(2 citation statements)

References 3 publications

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“…Velasco and Rowe26 proposed an NN for the automatic determination of process stability and the classification of unnatural patterns into categories such as upward trends, downward trends, cycle patterns, mixture patterns and stratification patterns.…”

Section: Application Of Nns For Pattern Recognitionmentioning

confidence: 99%

The use of neural networks in statistical process control charts

Psarakis

2011

Quality & Reliability Eng

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Neural networks (NNs) are massively parallel computing mechanism emulating a human brain. It has been proved that they had a satisfactory performance when they were used for a wide variety of applications. In the recent years, the efficiencies that provided the NNs also began to be applied in statistical process control (SPC). SPC charts have become one of the most commonly used tools for monitoring process stability and variability in today's manufacturing environment. These tools are used to determine whether the process is statistically under or out of control but in some cases such as the presence of autocorrelation as well as the presence of a specific pattern in the data do not provide the possibility of correctly and quickly detecting and classifying the existing fault. These problems have led many researchers to propose alternative methods for monitoring processes such as the use of NNs. In this paper, we discuss issues concerning the combination of both tools. Specifically, we study the NNs for the detection and determination of mean and/or variance shifts as well as in pattern recognition in the SPC charts. Furthermore, the use of NNs when the data are correlated is discussed. Finally, the use of NNs in multivariate control charts is addressed. The networks architectures that were used for each case, the way of operation and the performance of the proposed NNs applications are pointed out.

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Section: Application Of Nns For Pattern Recognitionmentioning

confidence: 99%

The use of neural networks in statistical process control charts

Psarakis

2011

Quality & Reliability Eng

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“…Other unnatural patterns to frequent X-bar control charts include mixtures, trends and sudden shifts. Previous works in the application of neural networks to control chart pattern recognition were aimed at identifying these kinds of patterns [2][3][4][5][6][7][8] . These works prove promising in solving the problem of recognizing mixtures, trends and sudden shifts on process control charts; however, they do not consider the identification of process instability.…”

Section: Unnatural Patternsmentioning

confidence: 99%

<title>Control chart pattern recognition using a back propagation neural network</title>

Spoerre¹,

Perry

2000

SPIE Proceedings

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In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). On-line automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. A back propagation artificial neural network is used to model X-bar quality control charts and identify process instability situations as specified by the Western Electric Statistical Quality Control Handbook. Results indicate that the performance of the back propagation neural network is very accurate in identifying these control chart patterns. This work is significant in that the neural network output can serve as a link to process parameters in a closed-loop control system. In this way, adjustments to the process can be made on-line and quality problems averted.

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A Novel Supervised Learning Model for Figures Recognition by Using Artificial Neural Network

Alfawaer

Al-Zoubi

2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Back propagation artificial neural networks for the analysis of quality control charts

Cited by 25 publications

References 3 publications

The use of neural networks in statistical process control charts

The use of neural networks in statistical process control charts

<title>Control chart pattern recognition using a back propagation neural network</title>

A Novel Supervised Learning Model for Figures Recognition by Using Artificial Neural Network

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