In order to monitor the machining status of a machine tool, it is necessary to measure the signal of the machine tool and establish the relationship between the machining status and the signal. One effective approach is to utilize an AIbased analysis model. To improve the accuracy and reliability of AI models, it is crucial to identify the features of the model through signal analysis. However, when dealing with time series data, it has been challenging to identify these features. Therefore, instead of directly applying time series data, a method was used to extract the best features by processing the data using techniques such as RMS and FFT. Recently, there have been numerous reported cases of designing AI models with high accuracy and reliability by directly applying time series data to find the best features, particularly in the case of AI models combining CNN and LSTM. In this paper, time series data obtained through a gap sensor are directly applied to an AI model that combines CNN, LSTM, and MLP (Multi-Layer Perceptron) to determine tool wear. The machine tool and tool status were monitored and evaluated through an AI model trained using time series data from the machining process.

Factors such as weight reduction and improved fuel efficiency of vehicles interfere with the efficiency of roller bearings in automobiles under harsh conditions. In particular, studies are ongoing to increase the load capacity and rigidity under highspeed conditions. The development of tapered roller bearings that can be used under high-speed conditions is accelerating. In the case of high-speed bearings, factors such as centrifugal force, gyroscopic moment, and slippage have a greater influence on the performance of the bearing, unlike the traditional operating mechanisms. The resulting lubrication characteristics have a profound impact on the failure mode of the bearing. In particular, unlike traditional roller bearings, system failure due to damage to the retainer frequently occurs, suggesting the need for prompt investigation. In this study, the rotational characteristics and strength of three models, a steel cage and two plastic cages for tapered roller bearings with the same internal structure, were examined. A comparative analysis of retainers with different shapes and materials can reveal the factors contributing to optimal performance under high-speed operating conditions and the optimal design of bearings.

Ball and roller bearings are commonly used machine elements for supporting rotating motion about shafts in simple devices including bicycles, in-line skates, and electric motors, as well as in complex machines. Heat is generated by the friction in the bearings, which causes the temperature inside the bearing to increase. If the heat is not appropriately removed from the bearing, elevated temperatures may give rise to premature failure. It is, therefore, important to be able to calculate the temperature in the bearings due to friction. Here, we describe a method to estimate the frictional torque in bearings using an empirical formula developed using a method based on bearing analysis tool and the measured frictional torque in a spindle system. Thermal analysis of the spindle system including the bearings was achieved using the finite element method (FEM), and the bearing temperature was compared with measured data to verify the empirical formula.