Manufacturing quality control by means of a Fuzzy ART network trained on natural process data

Pacella, Massimo; Semeraro, Quirico; Anglani, A.

doi:10.1016/j.engappai.2003.11.005

Cited by 54 publications

(43 citation statements)

References 19 publications

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“…Gomez and Chesnevar 27 applied Fuzzy ART neural network to pattern classification. Pacella et al 12 used a Fuzzy ART neural system for manufacturing quality monitoring. Peker and Kara 28 explained a parameter setting of the Fuzzy ART neural network to part-machine cell formation problem.…”

Section: Literature Review Of Fuzzy Artmentioning

confidence: 99%

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An alternative evaluation of FMEA: Fuzzy ART algorithm

Keskin

Özkan

2008

Quality & Reliability Eng

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Failure Mode and Effects Analysis (FMEA) is a technique used in the manufacturing industry to improve production quality and productivity. It is a method that evaluates possible failures in the system, design, process or service. It aims to continuously improve and decrease these kinds of failure modes. Adaptive Resonance Theory (ART) is one of the learning algorithms without consultants, which are developed for clustering problems in artificial neural networks. In the FMEA method, every failure mode in the system is analyzed according to severity, occurrence and detection. Then, risk priority number (RPN) is acquired by multiplication of these three factors and the necessary failures are improved with respect to the determined threshold value. In addition, there exist many shortcomings of the traditional FMEA method, which affect its efficiency and thus limit its realization. To respond to these difficulties, this study introduces the method named Fuzzy Adaptive Resonance Theory (Fuzzy ART), one of the ART networks, to evaluate RPN in FMEA.

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Section: Literature Review Of Fuzzy Artmentioning

confidence: 99%

“…Fuzzy ART operations reduce to ART1 (which accepts only binary vectors) as a special case. The generalization of learning both analog and binary input patterns is achieved by replacing the appearance of the logical AND intersection operator (∩) in ART1 by the MIN operator (∧) of fuzzy set theory 12 .…”

Section: Fuzzy Artmentioning

confidence: 99%

An alternative evaluation of FMEA: Fuzzy ART algorithm

Keskin

Özkan

2008

Quality & Reliability Eng

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“…Pacella et al [8] applied the adaptive resonance theory (ART) neural networks in their work. They have presented a fuzzy ART neural system for quality control.…”

Section: Introductionmentioning

confidence: 99%

Detecting flow meter drift by using artificial neural networks

Salamah¹,

Palaneeswaran²,

Savsar³

et al. 2011

Int. J. SDP

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In this paper, artifi cial neural networks (ANNs) were used to assess the performance of fl ow meters used in industrial water supply. These fl ow meters are susceptible to drift, a condition causing them to give erroneous readings that are inconsistent with the actual fl ow. A simulation of industrial water fl ow to the industrial consumers was made. This simulation contained both healthy and drifting fl ow meter readings. ANN was built and trained on the simulated data. At the time of testing, the ANN developed was correct 89.52% of the time in determining the status of the fl ow recorded by a fl ow meter. Keywords: artifi cial neural network, fl ow meter drift, industrial water supply, statistical process control. INTRODUCTIONThe importance of water cannot be overemphasized. Because of this, people realized the importance of controlling, organizing, and regulating its use. No sensible controlling, organizing, regulating, and planning can be done without accurate measurement. Hence, the abundance of fl ow meters in any water system. Accurate measurement arrangements are required for effectively managing water infrastructure regimes. Flow meters can be subject to specifi c failures such as erroneous readings and breakdown. Inaccurate measurements affect the controlling, organizing, and regulating processes of the water system and planning. Measures for avoiding and mitigating such fl ow meter problems include: calibration, repair, and replacement. Flow meter calibration generally involves checking against accurate standards to determine any appreciable deviations and correcting for errors. Yet, this calibration is not a one time transaction as it does not end the problem forever.Flow meter calibration is often neglected by the operation and maintenance staff of a water system. Drift is an error in measurement, which can increase with time. Ben Salamah et al.[1] outlined a statistical process control (SPC)-based fl ow meter drift detection method for the industrial water use regime. Realizing the usefulness of artifi cial neural network (ANN) modeling for forecasting and classifi cation objectives, an extension of this research considered specifi c applications of ANN modeling with improved SPC frameworks for fl ow meters. This paper presents some key fi ndings from the application of ANN models for detecting fl ow meter drifts.

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“…One of the main problems with run rules is that simultaneous application of all these rules is likely to result in excessive number of false alarms or incorrect recognition. Many other researchers (Pham and Oztemel, 1992b;Hwarng and Hubele, 1993;Cheng, 1997;Guh et al, 1999;Perry et al, 2001;Pacella et al, 2004;Guh and Shiue, 2005) have successfully applied artificial neural networks (ANN) and recognized various CCPs using raw process data. The advantage with neural network is that it is capable of handling noisy measurements requiring no assumption about the statistical distribution of the monitored data.…”

Section: Introductionmentioning

confidence: 99%

Feature-based decision rules for control charts pattern recognition: A comparison between CART and QUEST algorithm

Bag¹,

Gauri²,

Chakraborty³

2012

ijiec

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Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance.

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Manufacturing quality control by means of a Fuzzy ART network trained on natural process data

Cited by 54 publications

References 19 publications

An alternative evaluation of FMEA: Fuzzy ART algorithm

An alternative evaluation of FMEA: Fuzzy ART algorithm

Detecting flow meter drift by using artificial neural networks

Feature-based decision rules for control charts pattern recognition: A comparison between CART and QUEST algorithm

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