Comparison of K-means and GMM methods for contextual clustering in HSM

Abstract

Abstract High speed machining (HSM) is widely used for the manufacturing of aircraft structures, turbine blades, etc. It greatly increases the efficiency and automation for the machining. However, in HSM, operators cannot detect incidents when they manage several machines of a production cell. Robust monitoring systems are required to protect the machine tool and the high value added parts. In the global context of the Industry 4.0, abundant digital data is available in a modern manufacturing company and could be used to turn the machines-tools smarter and to support the decision making of the operational management. One of the first step of data mining approach is the accurate selection of relevant. To do so, the raw data need to be classified into different contextual clusters. This paper compares two different methods of the unsupervised classification of machining context: K-means and GMM (Gaussian Mixture Model). It was found that GMM method can classify correctly the machining context, whereas K-means is not suitable.