Towards Predictive Part Quality and Predictive Maintenance in Industrial Machining - A Data-Driven Approach

Abstract

Programs such as Industry 4.0 and Internet of Things contain the promise of "intelligent production" with "smart services". In fact, great advances have already been made in sensor technology and machine connectivity. Production plants continuously generate and communicate large amounts of data and have become "cyber-physical systems". However, the task of gaining knowledge from these large amounts of data is still challenging. Data generated by numerical control (NC) and programmable logic controllers (NC) comes in a raw format that doesn’t allow the application of analytical methods directly. Extensive preprocessing and feature engineering has to be applied to structure this data for further analysis. An important application is the timely detection of deviations in the production process which allows immediate reactions and adjustments of production parameters or indicates the necessity of a predictive maintenance action. In our research, we aimed at the identification of special deviant behavior of a grinding machine based on NC data. One finding wast the distinguishing the warm-up program from regular production and the other to recognize imprecise identification of the grinding process window. Both tasks could be solved with extensive preprocessing of the raw data, appropriate feature extraction and feature reduction, and the subsequent application of a clustering algorithm.