Data-based Quality Analysis in Machining Production:

Abstract

In this case study we investigate the potential of time series sequencing machine tool control data for quality prediction. A comparison of optimised feature vector based random forest classification models, trained on several sequences based on real drilling time series data is conducted. The results suggest that while sequence length has an inferior effect, the overlap of sequences yields great potential for effective classification, limited in practice by computational restrictions.