Visual Analysis of Machine Tool Operation Mode Correlation Based on Parameter Category Coding

Aiming at the problem that the machine tool operation data has many dimensions, the parameters relationship is complex, and its abnormal patterns and hidden correlation information are difficult to fully excavate, this paper proposed a visual analysis method for the correlation of CNC machine tool operation mode. Firstly, the parameter category encoding is carried out from the two aspects of the sliding window and time point of the machine tool operation data, and then the multi-parameter category encoding combination is clustered and association rule mining is carried out to extract the machine tool operation mode and parameter state association mode, and establish visual map. Integrating ease of use, flexibility, and interpretability, the visual analysis system MachineVis is further constructed, and a variety of interactive methods are designed to support users in discovering abnormal patterns of machine tool data, analyzing the changes of various parameters of machine tool data and capturing the relationship between each parameter. Finally, the validity and practicability of the system are proved by case studies.