Predicting machined surface quality under conditions of increasing tool wear

Introduction. The most important factor determining the efficiency of metal cutting is the quality of the surface of the part obtained during cutting. The surface quality of a machined part is directly dependent on the vibration activity of the cutting tool, the amplitude of which is influenced by the complex evolutionary dynamics of the cutting process. In light of this, modern digital twin technology, which allows predicting the surface quality values of the parts using virtual models, is becoming an extremely relevant way to improve the efficiency in metalworking control systems. The purpose of the work. This study aims to improve the prediction accuracy of a digital twin system for the surface quality of the machined parts under conditions of increasing cutting tool wear. The paper examines: the dynamics of the turning process of metal parts, as well as a mathematical model describing the dynamics of tool vibrations during metal machining on lathes, considering the influence of the thermodynamic subsystem of the cutting system. Research methods. An experimental approach was employed, utilizing a author-designed measuring stand along with a modern inverted metallographic microscope LaboMet-I version 4, equipped with wide-angle lenses 5/20, having a 20 mm linear field of view, and a digital camera for microscopes Ucam-1400 with a 1.4 μm×1.4 μm matrix, and a contour profile recorder T4HD. Furthermore, the study used mathematical modeling of the dynamic cutting system in the Matlab environment, for which the authors developed a specialized data processing program. Results and discussion. Curves depicting the tool wear rate, changes in the quality parameters of the machined surface as functions of cutting path, and as a function of cutting tool wear are constructed. Dynamic indicators suitable for parametric identification of virtual digital twin models are determined. The structure of these models is established, and parametric identification is performed. Numerical modeling is conducted in the Matlab environment, based on the results of which a curve depicting the change in average arithmetic surface roughness as a function of increasing tool wear is constructed. The convergence of the results of field and numerical experiments is evaluated, which shows a high reliability of the surface quality prediction achievable through the use of digital twin systems.