Erratum A-bar and R control chart interpretation using neural computing

Smith, Ann E.

doi:10.1080/00207549508930202

Cited by 23 publications

(37 citation statements)

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“…In input layer, the n first nodes correspond to the sample size used for process control. The remaining three nodes represent the statistics of the n observations which are their average X , their range R and their standard deviation S (a total of n+3 inputs) [9].A single neuron was required for output layer with the normalized coding shown in table 3. …”

Section: Neural Network Designmentioning

confidence: 99%

Using Artificial Neural Networks for Recognition of Control Chart Pattern

O¹,

A²,

S³

2015

IJCA

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Section: Neural Network Designmentioning

confidence: 99%

Using Artificial Neural Networks for Recognition of Control Chart Pattern

O¹,

A²,

S³

2015

IJCA

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“…The proposed model is faster than a CUSUM control chart which uses the information from an average run length by 20-40% in detecting the shift of a process. Smith [18] applied a back-propagation neural network to detect the variation in the process mean and its variance. However, this study does not consider the situation where the process mean and its variance vary simultaneously and does not evaluate the effectiveness of the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of variations in a multi-variate process using neural networks

Low

Hsu

2003

The International Journal of Advanced Manufacturing Technology

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In the area of quality control, several correlative quality characteristics may be considered simultaneously in a manufacturing process. In such a case, the Schewhart control charts neglect the influence of the correlation among these quality characteristics, leading to an incorrect judgment. Numerous methods have been proposed to control a process with multiple quality characteristics, however, most research emphasises only the variation of a process mean and devotes much less attention to the variation of a variance, relatively speaking. In this study, a neural network procedure is proposed for detecting variations in the variances, with the assumption that the mean value of the multiple quality characteristics of a process is under control. Two criteria, the average run length (ARL) and the average number of abnormal cases found, are used to evaluate the performance of the proposed procedure. The developed neural model is compared with the traditional |S| control method with regard to the aspect of detecting the variations in the variances in a process. The simulation results demonstrate the superiority of the proposed procedure in process control while multiple quality characteristics are simultaneously considered.

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“…Pugh's works (1989) might be the first to use NNs for detecting process mean shifts. Smith (1994) trained BPNs to detect both mean and variance shifts, and then to identify four simple abnormal patterns. Chang and Aw (1996) proposed an NN of fuzzy data representation and interpretation to detect and classify mean shifts.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

2009

Computers & Industrial Engineering

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Erratum A-bar and R control chart interpretation using neural computing

Cited by 23 publications

References 0 publications

Using Artificial Neural Networks for Recognition of Control Chart Pattern

Using Artificial Neural Networks for Recognition of Control Chart Pattern

Analysis of variations in a multi-variate process using neural networks

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

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