A novel approach for the improvement of error traceability and data-driven quality predictions in spindle units

Abstract: The lack of research on the impact of component degradation on the surface quality of machine tool spindles is limited and the primary motivation for this research. It is common in the manufacturing industry to replace components even if they still have some Remaining Useful Life (RUL), resulting in an ineffective maintenance strategy. The primary objective of this thesis is to design and construct an Exchangeable Spindle Unit (ESU) test stand that aims at capturing the influence of the failure transition of components during machining and its effects on the quality of the surface. Current machine tools cannot be tested with extreme component degradation, especially the spindle, since the degrading elements can lead to permanent damage, and machine tools are expensive to repair. The ESU substitutes and decouples the machine tool spindle to investigate the influence of deteriorated components on the response so that the machine tool spindle does not take the degrading effects. Data-driven quality control is another essential factor which many industries try to implement in their production line. In a traditional manufacturing scenario, quality inspections are performed to check if the parameters measured are within the nominal standards at the end of a production line or between processes. A significant flaw in the traditional approach is its inability to map the degradation of components to quality. Condition monitoring techniques can resolve this problem and help identify defects early in production. This research focuses on two objectives. The first one aims at capturing the component degradation by artificially inducing imbalance into the ESU shaft and capturing the excitation behavior during machining with an end mill tool. Imbalance effects are quantified by adding mass onto the ESU spindle shaft. The varying effects of the mass are captured and characterized using vibration signals. The second objective is to establish a correlation between the surface quality of the machined part with the characterized vibrations signals by Bagged Ensemble Tree (BET) machine learning models. The results show a good correlation between the surface roughness and the accelerometer signals. A comparison study between a balanced and imbalanced spindle along with its resultant surface quality is presented in this research.