Radial error motions of a spindle supported by an aerostatic journal bearing are investigated. The spindle units for analysis and experiment are consist of spindles having 0 lobe and 3, 5 and 9 lobes of 1.5 μm amplitude respectively and the housings having 0 lobe and 8 lobes of 1.5 μm amplitude respectively with two rows of 8 feed holes. The motion of the spindle is numerically solved with Runge-Kutta method and the force applied to the spindle is calculated by solving nonlinear unsteady Reynolds equation with FEM. The spindle motions are measured by capacitance type displacement transducers under free rotation, ranging 500 to 2500 min^-1. The numerical analysis program is verified experimentally for eight combinations of the spindle and the housing. It is shown analytically and experimentally that significant error motions occur, in particular, in the combinations of the 9-lobe spindle and the 0- and 8-lobe housings compared with other combinations.

A motor-driven gyroscopic power generator is developed that self-accelerates its spinning velocity by a power feedback. In our previous report, a power generation of 1.8 W was confirmed by a gyro generator, but an external power source was used to drive the spinning motor. In this research, a method is presented to apply the power generated by the precession movement to the spinning motor and accelerates the spinning velocity by the voltage feedback and boosting. In this paper, first, a mathematical model of the generator is presented and the relationship among the spin acceleration, generated power and the boosting ratio are numerically studied. Next the generated powers of 2.5" and 3.5" flywheel generators are calculated to show the possibility of 0.84W and 2.2W respectively. Finally, the validity of the theory is verified by the experiment.

This study presents the development of an automatic, low-cost, fast, and reliable non-contact system for surface roughness estimation and chatter vibration identification based on machine vision. The developed system focuses on predicting two critical problems in the machining process, which are surface roughness and chatter vibration, using images as input. Deep learning with convolutional neural networks is integrated into the system to bypass the feature extraction method traditionally used in conventional vision-based roughness and chatter predictions. Two systems are proposed: separate models that work specifically for each problem, and a combined model that aims to predict both problems in one process. Four deep learning architectures are proposed for both systems. The proposed models are built and tested on turned and milled surface datasets. A set of machining experiments are performed with various cutting conditions to generate the training and testing data. The result of the prediction model is then analyzed and compared with the measured data using a contact-based profilometer. The results indicate that the proposed system performs favorably in terms of accuracy and processing time, thus offering a promising alternative for quick inspection of surface quality.