Intelligent Quality Systems

Pham, Duc Truong; Öztemel, Ercan

doi:10.1007/978-1-4471-1498-7

Cited by 31 publications

(17 citation statements)

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“…Several examples will be provided in order to highlight possible areas and provide some hints on practical use of these systems. Pham and Oztemel (1996) reported their work on intelligent quality systems in their book. They have reported an expert system called XPC capable of performing statistical process control.…”

Section: Applications Of Intelligent Manufacturing Systemsmentioning

confidence: 98%

Intelligent Manufacturing Systems

Öztemel

2010

Springer Series in Advanced Manufacturing

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This chapter provides information on intelligent manufacturing systems including an analysis on change and historical progress of manufacturing systems as well as a brief review of traditional manufacturing systems. Fundamental technologies of artificial intelligence are also reviewed in order to establish the baseline for intelligent manufacturing systems. Following that, basic characteristics of intelligent manufacturing and respective architectures are provided. Some examples of the applications of intelligent manufacturing systems are highlighted.Keywords Artificial intelligence, manufacturing system, fuzzy logic, genetic algorithm, holonic manufacturing system IntroductionAutomation is one of the major indicators of the change in manufacturing. Machines behaving themselves not only decrease the cost but also produce the products to be more compliant with the needs and specifications of the customers. Although technologies such as flexible manufacturing systems provide various advantages, automation itself is not enough to provide competitive advantage. In most of the modern manufacturing facilities, machines are capable of making decisions and exhibiting intelligent behaviour. That is to say that the manufacturingrelated activities starting from the design to product shipment are being carried out through intelligent manufacturing technologies. As traditionally known, manufacturing systems are the integrated combination of various functions such as design, process planning, production planning, quality assurance, storing and shipment etc. In each of these functions several activities are carried out. Intelligent behaviour is exhibited by the machines in all of these functions. This diverted the attention of the researcher to "unmanned factories" and there have been quite interesting studies and implementations along this line. In this chapter, a general overview E. Oztemel ( ) Marmara University, Engineering Faculty,

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Section: Applications Of Intelligent Manufacturing Systemsmentioning

confidence: 98%

Intelligent Manufacturing Systems

Öztemel

2010

Springer Series in Advanced Manufacturing

View full text Add to dashboard Cite

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“…Many different neural network architectures and learning algorithms have been adopted for control chart pattern recognition systems, such as Backpropagation Networks (BPN) [17,4,9,8], Learning Vector Quantization (LVQ) Networks [17], unsupervised Adaptive Resonance Theory Network [18] and Modular Neural Networks (MNN) [4], etc. The authors of this paper have conducted a comparative study on five neural networks, i.e., BPN, LVQ, Radial Basis Function Networks (RBF), Probabilistic Neural Networks (PNN), and Generalized Regression Neural Networks (GRNN), for the CCP single pattern recognition.…”

Section: Neural Network For Pattern Recognitionmentioning

confidence: 99%

“…The parameters used to generate seven CCPs are based on Guh and Tannock's work [9,8], which are summarized in Table 1. Moving averages are taken for every 3 consecutive data points to smooth out the random noise in the simulated sample data [17]. x(t) = l + 3r * n(t) 600 …”

Section: Ccp Pattern Generation For Bpn Trainingmentioning

confidence: 99%

“…Recently, ANNs have been applied to pattern recognition for control charts [7,6,9,8,1,4,17]. In pattern recognition, ANNs has the capability to recall learned patterns from noisy data, making ANN a suitable tool for CCP recognition [9].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the coverage of these issues in the literatures is quite limited. Most researchers assume that only one particular pattern would be present during a period of time [17,4,9] in order to simplify the pattern recognition task. Guh [9] first presented a two-stage ANN approach for concurrent pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

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