Ensemble Model for Spindle Thermal Displacement Prediction of Machine Tools

Numerous factors affect the increased temperature of a machine tool, including prolonged and high-intensity usage,tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Consequently,spindle thermal displacement occurs, and machining precision suffers. To prevent the errors caused by thetemperature rise of the Spindle fromaffecting the accuracy during themachining process, typically, the factory willwarm up themachine before themanufacturing process.However, if there is noway to understand the tool spindle’sthermal deformation, the machining quality will be greatly affected. In order to solve the above problem, thisstudy aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practicalapplication, only a few temperature sensors are used to input the information into the prediction model for realtimethermal displacement prediction. This approach has greatly improved the quality of tool processing.However,each algorithm has different performances in different environments. In this study, an ensemble model is used tointegrate Long Short-TermMemory (LSTM) with Support VectorMachine (SVM). The experimental results showthat the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms.

Effects of automatic variable preload device on performance of spindle

To increase the machine accuracy by improving the stiffness of bearings,a preload was applied to bearings.A variable preload technology was necessary to perform machining processes in both low and high speed regions.An automatic variable preload device was fabricated using an eccentric mass.By installing the fabricated device on a spindle,the effect of the automatic variable preload device on the performance of the spindle was analyzed.In the results of the vibration measurement of the spindle,the vibration is increased by 20%-37% according to measurement points at the maximum rotation speed of 5 000 r/min.And,in the results of the noise measurement of the spindle,the spindle rotation speed is increased by about 1.9% and 1.5% at the front and side of the spindle,respectively.Based on the results of this analysis,an improved method that reduces such effects on the performance of the spindle is proposed.

ZERO TRANSMISSION AND ITS APPLICATION IN HIGH SPEED CNC MACHINE TOOLS

In order to realize high speed machining,the special requirements for the transmission and sturctrue of CNC machine tool have to be satisfied.A high speed spindle unit driven by a built-in motor is developed.An oil-water heat exchange system is used for cooling the spindle motor.The spindle is supported by Si_4N_3 ceramic ball angular contact bearings. An oil-air lubricator is used to lubricate and cool the spindle bearings.Some special structures are taken for balancing the spindle.

Novel method for detection of anomalous structure characteristics of ID precision ultrathin monocrystalline silicon section cutting tool

The structure characteristics of ID precision ultrathin monocrystalline silicon section cutting machine tool spindle with force monitoring bearings functioning as force measuring sensors were detected with the new Hilbert theory based signal wave envelope detection method, presented to replace the conventional hardware device in order to ensure that the signal is measured online with high fidelity. According to the probability of anomalous incidents in the cutting process, a mathematical recognition model has been designed and verified on an STC 22ID machine.

Imprecise Reliability Assessment for Heavy Numerical Control Machine Tools Against Small Sample Size Problem

Small sample size problem is one of the main problems that heavy numerical control(NC) machine tools encounter in their reliability assessment. In order to deal with the small sample size problem, many indirect reliability data such as reliability data of similar products, expert opinion, and engineers' experience are used in reliability assessment. However, the existing mathematical theories cannot simultaneously process the above reliability data of multiple types, and thus imprecise probability theory is introduced. Imprecise probability theory can simultaneously process multiple reliability data by quantifying multiple uncertainties(stochastic uncertainty,fuzzy uncertainty, epistemic uncertainty, etc.) together. Although imprecise probability theory has so many advantages, the existing natural extension models are complex and the computation result is imprecise. Therefore,they need some improvement for the better application of reliability engineering. This paper proposes an improved imprecise reliability assessment method by introducing empirical probability distributions to natural extension model, and the improved natural extension model is applied to the reliability assessment of heavy NC machine tool spindle to illustrate its effectiveness.