In the context of Industry 4.0, large volumes of manufacturing data are available on instrumented machine-tool. The critical point is the exploitation of this digital content. Data contextualization is important for efficient and robust data mining, particularly for industrial production. For this purpose, a classification method of the operational manufacturing context is proposed. It relies on knowledge integration (by business rules) and unsupervised machine learning, with a Gaussian Mixture Model, applied to in-process monitoring signals. The method was evaluated on real industrial machining databases collected during one year. Manual data mining shows that this method is accurate on industrial production (98.9%). Moreover, the application of contextual classifications for the chatter detection on the same production data shows the relevancy of the proposed data mining approach.
