Virtual tomography as a novel method for segmenting machining process phases with the use of machine learning-supported measurement

Abstract

A new idea of machine learning-based technological process segmentation with the use of multi-sensor measurement is proposed in this article. The proposed segmentation of the machining process through appropriate measurement data modelling provides valuable insight that is necessary for technological parameters optimization or predictive maintenance. By combining multi-sensor industrial measurement and data science, this new solution is a more advanced and effective way of determining qualitative characteristics of the machining process that must be taken into account when developing smart analytical approaches, such as digital twins. An experimental measuring system consisting of accelerometers and current transducers is described. The system is implemented on an industrial CNC machine in order to assess operating conditions of the spindle and axis drives during the milling process. A novel method of detecting and segmenting the milling phases is proposed, involving data preprocessing and time-series signals data analysis to detect specific patterns or features that are indicative of each phase of the milling process. When applied in smart structures or digital twins, the proposed segmentation method provides similar information to that obtained by tomography imaging; therefore, the new method is called as virtual tomography. The developed phase segmentation method is of vital practical importance in terms of industrial implementation of technological process measurement and diagnostic systems. Especially as it can provide valuable information concerning elementary cutting zones and their influence on the process efficiency or influence of the composite structure layers machining on the tool wear, not available using hitherto known methods.