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The purpose of this paper is to test and analyze the friction torque of double-row angular contact ball bearings under vacuum or ordinary pressure environment, horizontal or upright installation mode, and different rotational speeds, and to provide theoretical bases for the development of aerospace equipment.

The experiments were carried out to investigate the effects of vacuum or ordinary pressure environment, horizontal or upright installation mode and different rotational speeds on bearing friction torque. To explore the relationship between working conditions and bearing friction torque, firstly, based on the generation source of friction torque, the test principle was determined, a test system was developed and the reliability of data was verified. Secondly, the friction torque of bearing was tested, and the values under various working conditions were obtained. Finally, this paper compared and discussed the test results.

The test results show that the friction torque value of vacuum environment horizontal installation condition is the largest at different rotational speeds, and the rotational speed has the most significant influence on the friction torque.

The friction torque test system of double-row angular contact ball bearing under vacuum environment was designed and built. The influence rules of vacuum or ordinary pressure environment, horizontal or upright installation mode and different rotational speeds on bearing friction torque were obtained.

The peer review history for this article is available at: http://dx.doi.org/10.1108/ILT-08-2023-0259

The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

When processing 11Cr-3Co-3W martensitic heat-resistant steel, the traditional pouring cooling method often appears large cutting force, high cutting temperature, serious tool wear and poor surface quality. This paper aims to use new cooling methods for processing this problem.

Different performance indicators such as cutting force, tool wear and surface quality were measured and analysed under different continuous milling times. The relationship between liquid nitrogen flow and cutting force and surface roughness was analysed and measured.

The results show that with the increase of liquid nitrogen flow, the cutting force decreases, especially the Fx component, which decreases by 10%. When the liquid nitrogen flow reaches 8 L/min, the effect of increasing the liquid nitrogen flow on reducing the cutting force becomes smaller. The cutting force reduced by up to 15%, and the tool life increased up to 20% using liquid nitrogen cryogenic cooling than in cutting liquids cooling. When minimal quantities of lubricant (MQL) was added, the cutting force was reduced by 23%, and the tool life increased by 25%. When the cutting speed increases from 100 m/min to 250 m/min, the cutting force with cutting liquid cooling does not change significantly while the cutting force with liquid nitrogen cooling decreases with the cutting speed increasing. It shows that liquid nitrogen cooling is more suitable for high-speed machining. After the cutting length reaches 66 m, the surface roughness of the workpiece using liquid nitrogen cooling method larger than that of the cutting liquid cooling method. When MQL is added into liquid nitrogen, the lubrication performance is improved, and the surface roughness of the workpiece is reduced about 8%.

Many studies had focused on the improvement of tool life and surface quality by different cooling methods, or on the injection process and chip mechanism. However, there are few relevant studies on the variation of cooling and lubrication properties with the change of cutting length in liquid nitrogen cryogenic processing. In this research, different performance indicators such as cutting force, tool wear and surface quality were measured and analysed under different continuous milling times. The relationship between liquid nitrogen flow and cutting force and surface roughness was analysed and measured.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0053/

To understand the service performance of full ceramic ball bearings under extreme working conditions and improve their service life, dynamic characteristic tests of full ceramic ball bearings under ultra-low temperature conditions were carried out by a low-temperature bearing life testing machine, and temperature rise and friction were measured under extreme low-temperature environment.

The heat-flow coupling model of bearing was established by CFD software, and the test results were further analyzed.

The results show that the temperature rise of the bearing is not obvious in the liquid nitrogen environment. With the increase of the chamber temperature, the lubrication state of the bearing changes, resulting in the temperature rise of the outer ring of the bearing. As the temperature of the test chamber increases, the friction force on the bearing increases first and then decreases under the action of multifactor coupling.

The research results provide test data and theoretical basis for the application of all-ceramic ball bearings in aerospace and other fields and have important significance for improving the service life of high-end equipment under extreme working conditions.

The research results provide test data and theoretical basis for the application of full ceramic ball bearings in aerospace and other fields and have important significance for improving the service life of high-end equipment under extreme working conditions.

The research results provide test data and theoretical basis for the application of full ceramic ball bearings in aerospace and other fields and have important significance for improving the service life of high-end equipment under extreme working conditions.

The research results provide test data and theoretical basis for the application of full ceramic ball bearings in aerospace and other fields and have important significance for improving the service life of high-end equipment under extreme working conditions.

For a lightweight and accurate description of bearing temperature, this paper aims to present an efficient semi-empirical model with oil–air two-phase flow and gray-box model.

First, the role of lubricant/coolant in bearing temperature was discussed separately, and the gray-box models on the heat convection inside a bearing cavity were also created. Next, the bearing node setting scheme was optimized. Consequently, a novel semi-empirical two-phase flow thermal grid for high-speed angular contact ball bearings was planned. With this model, the thermal network for the selected motored spindle was built, and the numerical solutions for bearing temperature rise were obtained and contrasted with the experimental values for validation. The polynomial interpolation on test data, meanwhile, was also performed to help us observe the temperature change trend. Finally, the simulations based on the current models of bearings were implemented, whose corresponding results were also compared with our research work.

The validation result indicates that the thermal prediction is more accurate and efficient when the developed semi-empirical oil–air two-phase flow model is employed to assess the thermal change of bearings. Clearly, we provide a more proper model for the thermal assessment of bearing and even spindle heating.

To the best of the authors’ knowledge, this paper introduced the oil–air separation and gray-box model for the first time to describe the heat exchange inside bearing cavities and accordingly presents an efficient semi-empirical oil–air two-phase flow model to evaluate the bearing temperature variation by using thermal network method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2023-0180/

This paper aims to improve the wear resistance of titanium alloy using a high-hardness boride layer, which was fabricated on Ti6Al4V by a high-temperature boronizing process.

The boride layers on Ti6Al4V were obtained at 1000°C for 5–15 h. Scanning electron microscopy, energy dispersive analysis and X-ray diffractometer were used to characterize the properties of the boride layer. The tribological performance of the boride layer at room and elevated temperatures was investigated.

The X-ray diffraction analysis showed that the boride layers were a dual-phase structure of TiB and TiB₂. When the boronizing time increased from 5 h to 15 h, the microhardness increased from 1192 HV_0.5 to 1619.8 HV_0.5. At 25°C and elevated temperatures, the friction coefficients of the boride layers were higher than that of Ti6Al4V. The wear track areas of T-5 at 200°C and 400°C were 2.5 × 10^–3 and 1.1 × 10^–3 mm², respectively, which were 6.1% and 2.6% of that of Ti6Al4V, indicating boride layer exhibited a significant wear resistance. The wear mechanisms of the boride layer transformed from slight peeling to oxidative wear and abrasive wear as the temperature was raised.

The findings provide an effective strategy for improving the wear resistance of Ti6Al4V and have important implications for the application of titanium alloy in a high-temperature field.

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.