Abstract
Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.
Highlights
In a study by the Leibniz Centre for European Economic Research (ZEW), it was carried out that digitalization in size companies (SME) in Germany is progressing slowly (SME: small and mediumsized enterprise [1] (p. 4), [2]
The research technique is to increase the transparency of changeover processes to support the availability management on the shopfloor. This shall be achieved by Machine Learning approaches, which are applied to identify changeover processes out of a big dataset generated by external sensors, which are attached to real production machines
In a former article, it was described that changeover times are one of the main reasons to decrease the Overall Equipment Effectiveness (OEE) of production facilities
Summary
In a study by the Leibniz Centre for European Economic Research (ZEW), it was carried out that digitalization in SMEs in Germany is progressing slowly (SME: small and mediumsized enterprise [1] (p. 4), [2]. The transparency of the changeover process to facilitate availability management so far is limited. The research technique is to increase the transparency of changeover processes to support the availability management on the shopfloor. This shall be achieved by Machine Learning approaches, which are applied to identify changeover processes out of a big dataset generated by external sensors, which are attached to real production machines.
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