ABSTRACT 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.

中文翻译：

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基于高斯混合模型的无监督机器学习的智能加工上下文分类

摘要 智能机床产生大量的数字数据。数据挖掘可以支持运营管理的决策。数据挖掘方法的第一步是选择相关数据。因此，原始数据必须被分类到不同的上下文组中。本文提出了一种基于高斯混合模型的无监督机器学习的智能加工数据的原始上下文分类。最佳类数由基于贝叶斯信息准则的轮廓方法确定。该方法在来自航空航天工业的四种不同机床的真实数据上得到了验证。手动数据挖掘和 k 折交叉验证证实所提出的方法提供了良好的上下文分类结果。然后，使用这种上下文分类计算了几个关键绩效指标。它们显示了该方法的相关性。