Contextual classification for smart machining based on unsupervised machine learning by Gaussian mixture model

Intelligent machine-tools generate a large amount of digital data. Data mining can support decision-making for operational management. The first step in a data mining approach is the selection of relevant data. Raw data must, therefore, be classified into different groups of contexts. This paper pro...

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Bibliographic Details
Published inInternational journal of computer integrated manufacturing Vol. 33; no. 10-11; pp. 1042 - 1054
Main Authors Wang, Zhiqiang, Ritou, Mathieu, Da Cunha, Catherine, Furet, Benoît
Format Journal Article
LanguageEnglish
Published Taylor & Francis 01.11.2020
Subjects
BIC
Engineering Sciences
GMM
Industry 4.0
machining
Mechanical engineering
Mechanics
unsupervised machine learning
unsupervised machine learning
Industry 4.0
machining
BIC
GMM
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Summary:Intelligent machine-tools generate a large amount of digital data. Data mining can support decision-making for operational management. The first step in a data mining approach is the selection of relevant data. Raw data must, therefore, be classified into different groups of contexts. This paper proposes an original contextual classification of data for smart machining based on unsupervised machine learning by Gaussian mixture model. The optimal number of classes is determined by the silhouette method based on the Bayesian information criterion. This method is validated on real data from four different machine-tools in the aerospace industry. Manual data mining and k-fold cross-validation confirm that the proposed method provides good contextual classification results. Then, several key performance indicators are calculated using this contextual classification. They show the relevancy of the approach.
ISSN:0951-192X
1362-3052
DOI:10.1080/0951192X.2020.1775302