A method of NC machine tools intelligent monitoring system in smart factories

•The paper introduces a framework of bi-directional data and control flow between machine tools and upper-level applications;•Several key problems in this field are presented, and corresponding solutions are subsequently analyzed and discussed;•To bridge the gap between the monitoring data and higher applications, the means of monitoring data processing and analysis is described;•Based on the uniform data bus, an NC machine tool intelligent monitoring and data processing system in smart factories is developed, which validates the concept and performs well in the complex manufacturing environment. The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories.