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AuthorYeganeh, Ali
AuthorAbbasi, Saddam A.
AuthorPourpanah, Farhad
AuthorShadman, Alireza
AuthorJohannssen, Arne
AuthorChukhrova, Nataliya
Available date2023-05-28T10:11:27Z
Publication Date2022
Publication NameExpert Systems with Applications
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.eswa.2022.117572
URIhttp://hdl.handle.net/10576/43500
AbstractProfile monitoring is a challenging issue in statistical process control (SPC). It aims to use a functional relationship between a response variable and one or more explanatory variable(s) to summarize the quality of a process/product. Most of the existing studies consider the same form of a functional relationship for both in-control (IC) and out-of-control (OC) situations or parametric approaches. However, non-parametric profiles with different relationships in OC conditions are very common. In this paper, we propose a novel ensemble framework to monitor changes in both regression relationship and variation of the profile for Phase II applications. This approach employs a pool of artificial neural networks (ANNs) as learners to enhance the efficiency of a base control chart, which is a non-parametric exponentially weighted moving average (NEWMA) in this study. Then, a further ANN is used as a reasoning scheme (incorporator) to perform prediction by combining the outcomes of the learners. Experimental results demonstrate the effectiveness of the proposed framework, denoted by EANNN, in comparison with the base control chart, i.e., NEWMA, and other non-parametric methods. In addition, a practical example regarding a deep reactive ion-etching process from semiconductor device fabrication is provided to show the implementation of the proposed method. 2022 Elsevier Ltd
Languageen
PublisherElsevier
SubjectArtificial neural network
Control chart
Ensemble learning
Non-parametric scheme
Profile monitoring
Statistical process control
TitleAn ensemble neural network framework for improving the detection ability of a base control chart in non-parametric profile monitoring
TypeArticle
Volume Number204
dc.accessType Abstract Only


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