The problem of chip breaking and evacuation is the key point of staggered teeth boring and trepanning association (BTA) drilling. The factors that influence chip breaking with staggered teeth BTA deep hole drilling are analyzed by using the chip bending deformation mechanism for chip formation and flow through the rake face and chip breaker. This study investigated the distribution and variation of chip deformation and breaking along drilling conditions, with respect to drilling radius, drilling process parameters, tool wear, and chip breaker geometric parameters. The results show that the tool-chip contact length is about 1.65 times the chip thickness in staggered teeth BTA drilling. The cutting radius of the teeth has a considerable influence on the chip thickness. Compared with the drilling speed, the feed has a greater impact on chip deformation and breaking, and the chip thickness and strain increase with increased feed. Increased drilling depth and tooth wear aggravates the friction state between the chip and the rake face, augments chip thickness and tool-chip contact length, and increases the chip’s strain increment. As the width of chip breaker decreases and the height increases, the chip strain increases and the breaking conditions are improved.

An improved density-based spatial clustering of applications with noise (IDBSCAN) analysis approach based on kurtosis and sample entropy (SE) is presented for the identification of operational state in order to provide accurate monitoring of spindle operation condition. This is because of the low strength of the shock signal created by bearing of precision spindle of misalignment or imbalanced load, and the difficulties in extracting shock features. Wavelet noise reduction begins by dividing the recorded vibration data into equal lengths. Features like kurtosis and entropy in the frequency domain are used to generate feature vectors that indicate the bearing operation state. IDBSCAN cluster analysis is then utilized to establish the ideal neighborhood radius (Eps) and the minimum number of objects contained within the neighborhood radius (MinPts) of the vector set, which are combined to identify the bearing operating condition features. Finally, utilizing data from the University of Cincinnati, the approach was validated and assessed, attaining a condition detection accuracy of 99.2%. As a follow-up, the spindle’s vibration characteristics were studied utilizing an unbalanced bearing’s load bench. Bearing state recognition accuracy was 98.4%, 98.4%, and 96.7%, respectively, under mild, medium, and overload circumstances, according to the results of the experimental investigation. Moreover, it shows that conditions of bearings under various unbalanced loads can be precisely monitored using the proposed method without picking up on specific sorts of failures.

In this paper, a quasi-static angular contact ball bearing model, considering assembly accuracy is constructed, while a numerical solution method for bearing stiffness under bad assembly state is established. A 7014C angular contact ball bearing is used as the research object and five groups of different spacer inclinations are designed to imitate the installation error of the spindle bearing. The bearing stiffness performance was comparatively analyzed, according to the five spacers. The effect of preload and rotation speed on bearing stiffness are systematically investigated, considering different parallelism errors, as induced by the spacers. The influence mechanism of the badly assembled bearing on the respective stiffness anisotropy is studied based on the proposed model. The results show that the variations of the inclination between the inner and outer rings of the bearing exhibit a very weak effect on the axial stiffness, while the influence on the radial and angular stiffness is more significant.

The development of coal–water slurry (CWS), a new type of coal-based chemical product in China, has garnered increasing attention as a potential substitute for petroleum resources. The Coriolis mass flow meter is widely used in industrial measurement due to its low uncertainty and its ability to simultaneously measure fluid density and mass flow rate, with a single-phase measurement error as low as 0.1%. However, significant errors still exist in multiphase flow measurement scenarios. To address this issue, we designed and constructed a CWS liquid–solid two-phase flow measurement platform to investigate the flow measurement errors of CWS in Coriolis mass flow meters under various conditions. A deep learning correction framework was developed to mitigate the significant measurement errors in liquid–solid two-phase flow. Based on the theoretical support provided by repeatability experiments, two correction models were established: (1) An error correction model based on a BP neural network was developed, which provided corrections for the measurement errors of CWS liquid–solid two-phase flow. The first correction results showed that the corrected error of the predictive model was 3.98%, a significant improvement compared to the 5.11% error measured by the X company’s meter. (2) Building on this, a second correction model was established through algorithm optimization, successfully reducing the corrected error of the predictive model to 1.01%. Through this study, we aim at providing a new technical approach for Coriolis mass flow meters in the field of liquid–solid two-phase flow measurement, enhancing measurement accuracy, reducing costs, and offering more reliable data support for industrial process control and scientific research.