Flexible pressure sensors have become pivotal in the advancement of wearable electronics and underwater monitoring, particularly when augmented by artificial intelligence. Nevertheless, the development of a unified sensing platform capable of seamless operation in both health monitoring and underwater communication remains a significant challenge. To address this issue, a highly sensitive flexible iontronic pressure sensor featuring a micro-pyramidal architecture was developed. The device is fabricated using molding involving a bespoke composite ink comprising carbon nanotubes (CNTs) and ionic liquid as the sensing layer. This layer is then sandwiched between screen-printed silver electrodes. The sensor demonstrated exceptional performance metrics, including high sensitivity (370 kPa−1), rapid response and recovery times (20 ms), and outstanding reliability (about 20000 cycles). In the domain of wearable health monitoring, the sensor demonstrated its capacity to discern faint human pulse signals, facilitating the acquisition of high-fidelity pulse waveforms. Concurrently, within the domain of underwater intelligent communication, the sensor was used to detect Morse code signals, which were then accurately classified by a deep learning algorithm. This work not only validates the sensor’s high performance but also demonstrates its dual functionality, seamlessly connecting human healthcare with intelligent underwater interaction and significantly broadening the scope of flexible sensing applications.

Inconel 718, a nickel-based superalloy essential for aerospace components, presents significant machining challenges due to excessive cutting forces and residual stresses that impair surface quality and dimensional precision. This study introduces a novel multi-objective optimization framework for milling Inconel 718, pioneering the first use of C60 nanofluid minimum quantity lubrication (NMQL) to leverage the exceptional thermal conductivity and lubricity of C60 nanoparticles. A coupled prediction model for cutting force and residual stress, incorporating NMQL’s lubrication and cooling effects, was developed and optimized using a diversified mutation-driven particle swarm optimization (MOPSO) algorithm to balance cutting force, residual stress, and material removal rate (MRR). Experimental validation on a vertical machining center confirms high model accuracy, with cutting force prediction errors below 6.73

This study introduces a dual-resonator quartz pressure sensor employing a novel stress redistribution mechanism with orthogonal force beams. Utilizing the anisotropy of AT-cut quartz, the design integrates X- and Z-resonators with force paths parallel and perpendicular to the X-axis, respectively. This configuration induces opposite pressure sensitivities, yielding a high-gain differential output. Finite element analysis guided the geometric optimization for maximum sensitivity and robustness. The fabricated device achieves a record differential sensitivity of 378.72 Hz/MPa at 25 degrees C, increasing to 413.84 Hz/MPa at 80 degrees C over 0-10 MPa. This positive temperature coefficient of sensitivity, combined with excellent linearity, low hysteresis (<0.18% FS), and high thermal stability, underscores its potential for demanding applications like downhole monitoring, advancing resonant sensor design through strategic mechanical engineering.

Wear debris in lubricating oil provides critical insights into the operational condition and health of mechanical equipment. A simple, low-cost solid-liquid triboelectric sensor is developed to enable real-time detection and discrimination of wear particle size, concentration, and material type, including ferromagnetic, non-magnetic iron, and copper powders. By integrating time- and frequency-domain feature extraction with principal component analysis and one-vs-one support vector machine classification, the system achieves recognition accuracies exceeding 97 % across all particle types and sizes. The sensor effectively identifies wear severity and lubrication status while enabling inference of the originating components, offering actionable intelligence for predictive maintenance and early fault diagnosis. This approach presents a versatile, self-contained strategy for monitoring the health of oil-lubricated machinery.

ObjectiveWith the increasing demand for the load-bearing capacity and service life of face gear transmission systems in the industrial field, a design method for face gear pair transmission based on equiangular spiral tooth profile was proposed to further enhance the load-bearing performance of face gear transmission.MethodsThe formation principle of the tooth surface of face gear with equiangular spiral was studied, and the discrete model of the tooth surface of face gear with equiangular spiral was derived by solving the meshing parameters of the meshing inlet point, meshing outlet point, the top point of the inner tooth profile and the root point of the outer tooth profile. The correctness of the tooth surface model was verified by using the numerical computation examples. The key characteristic parameters such as principal curvature, sliding rate and pressure angle of face gear with equiangular spiral were analyzed to prove that the tooth profile of face gear which are conjugated through the tooth profile of cylindrical gear are still equiangular spiral. The theoretical analytical model for the contact stress and bending stress of face gear was derived, and the accuracy of the theoretical model was verified through simulation.ResultsThe simulation results show that the stress of equiangular spiral face gear is significantly smaller than that of involute face gear. It is concluded that the load bearing capacity of equiangular spiral face gear is significantly higher than that of involute face gear. This study provides a new design idea for improving the load bearing capacity of face gear transmission.

The gradient of the ionic conductivity is a critical factor for assessing the spatial variability of ionic distributions, as evident in the complex dynamics that are characteristic of various aqueous environments. Accurate observations of the conductivity fluctuations resulting from ion movement in solutions are essential for determining the purity of the aqueous phase and the surrounding environment. This study presents a comprehensive overview of nondestructive immersion measurements of ionic conductivity gradients. The phenomena associated with these gradients arise from intrinsic differences in the medium physical properties, the wide variety of ion characteristics, the intricacies of interfacial conditions, and the interactions between chemical mass transport phenomena in aqueous settings. A thorough review of the latest advancements in ionic conductivity gradient detection methodologies based on electrical, optical, and acoustic principles is presented, highlighting the key milestones and breakthroughs achieved to date. Furthermore, the ionic conductivity gradient formation principles and underlying mechanisms are reviewed, and different electrode measurements, inductively coupled systems, optical refractometry systems, and acoustic velocity evaluation measurements are classified. Additionally, the applications of nondestructive immersion measurements in water quality monitoring, oceanographic research, chemical production, industrial process control, and other ion-involving electrochemical reactions are summarized. The prospects and future directions for enhancing the cost effectiveness of field nondestructive testing and long-term stability of the equipment using new materials, structures, and AI technologies are also discussed. The technological innovation in nondestructive immersion measurements is expected to catalyze transformative changes in the optimization of chemical production, ushering in a new era of efficiency and control in industrial processes.

Nickel-based superalloys exhibit poor machinability, leading to severe tool wear, unsatisfactory surface finish, and low processing efficiency. To address the existing issues, fullerene C60 nanofluid cutting fluid is used during the milling of nickel-based superalloys to improve cutting performance and clarify the metal processing mechanisms under various lubrication conditions. A thermodynamic coupling modelling method for milling, considering the friction reduction and cooling effects of minimum quantity lubrication (MQL), is proposed. According to the theory of oblique cutting and the imaginary heat source method, a differential unit model of cutting force and temperature was constructed. The model divides the cutting process into multiple small units, each of which generates cutting force and heat under the action of the cutting tool. In these units, the shear flow stress is calculated through the Johnson–Cook material constitutive model, which comprehensively considers the residual stresses caused by mechanical and thermal stresses. To demonstrate the accuracy of the established model, a series of milling experiments was performed to evaluate the cutting force, temperature, and residual stress under different lubrication modes. The experimental results indicate that, compared to dry cutting, flood cooled lubrication, and MQL, using C60 nanofluid minimum quantity lubrication (NMQL) can significantly reduce surface residual stress, with reductions of approximately 41.6

In this paper, a dual-harmonic-mode excited resonant pressure sensor with an AT-cut all-quartz sensing element was developed to synergistically achieve a wide measurement range for both temperature and pressure, along with a fast transient response. In this device, the excited third overtone mode indicated the applied pressure, while the beat frequency generated by the overtone and fundamental modes was used for self-temperature compensation. At the same time, the sandwiched all-quartz sensitive structure design effectively addressed the limitations associated with narrow pressure and temperature ranges. Especially, the dual-mode self-temperature sensing mechanism has been validated in the subzero temperature range for the first time. Quartz crystal processing techniques were employed to manufacture the sensor, and key performance metrics were characterized. The experimental results demonstrated that the sensor achieved an accuracy of +/- 0.015% full scale (FS) under a pressure range of 0-120 MPa and a temperature range of-40 to 150 degrees C. The pressure sensitivity of the overtone frequency was quantified as 365 Hz/MPa, and the beat frequency provided a temperature accuracy of 0.4 degrees C. Additionally, the developed sensor exhibited fast transient response under dynamic pressure and temperature conditions attributed to its self-temperature compensation capability. Thus, this study provides a practical solution for high-pressure measurements across a wide temperature range, combined with excellent transient performance.

Packaged temperature sensors exhibit significant thermal hysteresis effects, which directly impact their dynamic performance and accuracy during rapid temperature variations in marine environments. This article focuses on platinum resistance thermometers (PRTs), which are commonly used in oceanic applications, and a dynamic testing method was proposed that employs temperature step excitation within a fully liquid environment, along with a full range dynamic error compensation approach based on the fireworks algorithm (FWA). Initially, a dynamic testing system was developed, and its testing repeatability was verified. Subsequently, a sample database for PRTs at various temperature steps was created. By optimizing the fitness function, the FWA was utilized on the sample database during the iterative process to design a dynamic error compensation filter. The resulting compensation filter demonstrated enhanced universality across various temperature step sizes within the sensor measurement range. Through the filter's compensation, the rise time of the packaged PRT was reduced from an average of 477-121 ms within the measurement range. Furthermore, the dynamic response characteristics of the packaged PRT closely resembled those of the bare PRT. The dynamic testing method, which simulates heat transfer in real-world scenarios, in conjunction with the dynamic compensation method introduced in this article, can also be employed to achieve dynamic compensation for sensors operating under diverse testing principles.

Pipeline leak detection technologies have been playing a crucial role in protecting the safety of pipeline delivery. However, there is a lack of effective leak detection solutions for high-density interwoven pipelines in high-value industrial equipment and facilities. To tackle this challenge, we have focused on developing a quartz resonant pressure transducer with high sensitivity and high resolution for detecting leaks in the mentioned pipeline. The pressure-sensitive resonator achieves a high-quality factor of 1,041,555 with a series resonance frequency of 7220.3 kHz. The fabricated resonator is packaged with a customized crystal oscillator circuit using a stainless-steel housing. The packaged pressure transducer provides a typical sensitivity of 368.4 Hz/MPa, a resolution of 0.00025 %, and an accuracy of 0.02 % over a 40 MPa full scale. As a test bed for leak detection, a section of interwoven nylon pipeline with an adjustable length of up to 200 m is constructed. Novel leak detection algorithms based on high-quality pressure-sensitive signals are developed. Signal analysis demonstrates that the transducer can capture critical leak information, i.e., identifying the leak, assessing leak velocity, locating the leak, predicting dynamic leak, and detecting a tiny leak. The measurement results indicate that, based on the superior performance of the reported pressure transducers, it is feasible to construct an early and accurate health monitoring system for a complex pipeline network.

The impact of temperature on instrument measurements is widespread. To mitigate the significant measurement errors caused by temperature variations affecting board-level circuits during in-situ seawater conductivity measurements, a series of experiments are conducted using a seven-electrode conductivity sensor as a case study. A multivariate polynomial regression algorithm is employed for temperature compensation. After designing the instrument structure, sensor, and circuitry, six pure resistors are used to simulate the conductivity cell and experimentally evaluate the effects of temperature changes on the circuit. After calibration at the Institute of Ocean Engineering in Qingdao, China, test results indicate that, within the conductivity range of 30.678-67.214 mS/cm, covering most seawater environments, the instrument's accuracy improves from +/- 0.007 mS/cm to +/- 0.002 mS/cm after implementing the temperature compensation model over a temperature range of -10 degrees C to 40 degrees C. Results demonstrate that the proposed compensation method effectively reduces temperatureinduced drift and enhances measurement accuracy.

The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when the cutting conditions undergo any changes. This method has no application value if the cutting conditions frequently change. This manuscript proposes a method for monitoring tool wear based on unsupervised deep transfer learning. Due to the similarity of the tool wear process under varying working conditions, a tool wear recognition model that can adapt to both current and previous working conditions has been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibration signals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neural network (CNN) with a multi -layer perceptron (MLP). To achieve distribution alignment of deep features through the maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of the network. A platform for monitoring tool wear during end milling has been constructed. The proposed method was verified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoV steel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracy of over 80%. In comparison with the most advanced tool wear monitoring methods, the presented model guarantees superior performance in the target domains.

Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of −10–40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.

Previous surface topography prediction models for external spline shaping do not consider the relationship between the tool and workpiece geometry and cutter edge wear. In this work, by considering the tool–workpiece geometry correlation, a model for the tooth surface topography of the external spline is established based on the micro-element method. Combined with adhere model, a gear shaper wear model is founded on the wear mechanism of cutter edge micro-element derived from the abrasive wear mechanism. The distribution characteristics of tooth topography are analysed through an external spline shaping experiment, which verifies the validity of the presented model. For the uneven distribution of tooth surface topography, a shaping process optimisation algorithm is proposed based on the advance-and-retreat method, which can obtain tooth surfaces with uniform surface topography distribution and reduce tool wear. This work can guide the optimisation of the external spline shaping process, improve the integrity of the tooth surface and prolong the serviceable life of gear shaper.

Quartz resonant pressure sensors have emerged as a promising technology for precise pressure measurements in a variety of applications such as aerospace, meteorological observation, and energy exploitation. These sensors are inherently compatible with digital techniques and provide high accuracy, high resolution, and good long-term stability over a wide pressure range. These excellent features can be tentatively attributed to the mechanical and electrical properties of quartz crystals. However, there are no reviews that delve into the mechanisms underlying the superior performance of such sensors, leading to a bottleneck in technological development. Accordingly, this paper reviews the key physical properties and effects of crystalline material that have driven sensor technology advances. In particular, it is gathered that crystal cuts significantly influence the operation and performance of the sensors. These cuts can be classified into three categories based on the sensitivity to the measurement: pressure-sensitive, temperature-sensitive, and frequency-reference cuts. We also summarize the mechanisms that govern the optimization of the crystal cuts and the effect of the combinations of different cuts on performance improvement. Additionally, the unique physical effects of quartz crystals are presented and their role in the sensor technology innovation is discussed in detail. Next, the different quartz resonant pressure sensing technologies available are classified according to the acoustic wave transmission method: surface acoustic wave, vibrating beam, and vibrating diaphragm types. We conclude this paper with an analysis of state-of-the-art sensor technologies, as well as a discussion on the status of the sensor industry and the latest technology trends.

Considering the influence of manufacturing parameters, precisely predicting the post-grinding tooth surface topography is of great significance for improving transmission performance and service life of face gear transmission. However, it faces numerous challenges in practical applications, encompassing factors like abrasive grain size, grain distribution, grinding wheel inclination, and machine tool vibrations. Simultaneously, the establishment of prediction models presents complexities and precision-related difficulties. To predict the tooth surface topography more accurately, this paper proposes a method for predicting surface topography of face gear grinding based on dynamic contour interference sampling. Based on Hertzian contact theory, coupled with abrasive grain interference sampling, the motion trajectories of abrasive grains within discrete grinding width intervals are simulated. Thus, by considering the overlap regions of grinding width between adjacent grinding trajectories, a more accurate prediction of tooth surface topography is achieved. The method delves deeply into the impact of grinding depth on the deformation patterns within the grinding contact region. To validate the accuracy of the proposed method, face gear grinding experiments were designed and conducted, with the experimental results being compared against the predicted outcomes. The experimental results indicate that the topography prediction model, which accounts for grinding trajectory interference, closely aligns with the actual tooth surface topography.

This article presents the design methodology, fabrication process, and experiment evaluation of a bionic vector turbulence sensor with a combined stress-dispersed crossbeam and bullet-shaped package structure for turbulence measurements in harsh marine environments. The sensor is primarily designed based on the optimized reliability using the finite-element method. Specifically, a stress-dispersed cilium-beam sensitive structure with excellent mechanical robustness is developed by optimizing the stress concentration regions on the beam. Compared to traditional sensitive elements, a significant decrease in concentrated stress can be achieved, and the failure limit considerably increases. The crossbeam microstructure is fabricated based on the microelectromechanical systems (MEMSS) technology, and piezoresistors are formed on Integrating cilium Packaging the beams. The bullet-shaped package structure achieves high-pressure resistance by employing a double water- tightness approach. In particular, the design of open front fairwater limits excessive displacement of cilium to avoid structural destruction due to overload. The calibration results indicate that the sensitivity of the sensor reaches 1.06 x 10(-2) (V.m.s(2))/kg. A systematic evaluation of the sensor's marine environment adaptability, reliability, and stability is conducted to validate the robust performance of the designed sensor. Furthermore, a field test is performed in a real environment. The obtained results suggest that the reported sensor exhibits excellent environmental adaptability and reliability. The success of this investigation paves the way for the technology maturity and engineering application of the MEMS turbulence sensor.

Friction and wear phenomenon is a complex nonlinear system, and it is also a significant problem in the process of metal cutting. In order to systematically analyze the friction and wear process of tool material-workpiece material friction pair in the cutting process of high hardness alloy steel under different lubrication conditions, the chaotic characteristics of friction process between high hardness alloy steel and cemented carbide under the lubrication C60 nano-particles fluid are studied based on the chaos theory. Firstly, the friction and wear experiments of the friction pair between high hardness alloy steel and cemented carbide tool are carried out based on the ring-block friction and wear tester, and the results of friction force signal in time domain and wear width are obtained. Then, the friction signals in time domain are processed and transformed based on phase space reconstruction and recurrence plot theory, and the recurrence plots of different experimental groups under different lubrication conditions are generated. The evolution law of recurrence plot is further observed and studied, and the recursive quantitative index is analyzed. Finally, the cutting experiments of tool wear are carried out. The results show that the proposed method can intuitively and accurately reveal the wear evolution process and the wear feature identification law of the tool material-high hardness alloy steel pair under different lubrication conditions. Meanwhile, it is found that when the concentration of C60 nanoparticles is 200~300 ppm, the stability of the friction pair system is best. The proposed method can provide a strategy for wear prediction in cutting process, and provide a theoretical basis and technical support for antifriction lubrication methods in practical cutting applications.

The cutting characteristics of Inconel 718 alloy are high hardness and surface hardening, resulting in fast tool wear, severe chipping, and inadequate machining accuracy. To overcome these challenges, this article proposes a method to enhance the cutting performance by injecting fullerene C60 nanoparticle cutting fluid with minimum quantity lubrication (MQL) into the cutting zone. Leveraging the Johnson-Cook constitutive model and the imaginary heat source method, this study simulates the cooling effect and friction reduction characteristics of the cutting contact interface under minimum quantity lubrication conditions, and assessment of cutting energy consumption using predicted and measured specific cutting energy (SCE). Through friction wear tests, the friction coefficient changes under various lubrication conditions are measured and analyze the impact of lubrication conditions on friction and wear mechanism. The cutting test results reveal that variations in cutting parameters significantly influence energy efficiency, with specific cutting energy exhibiting a downward trend as the material removal rate (MRR) increases. Notably, C60 nanoparticle minimum quantity lubrication (NMQL) stands out excellent friction reduction and cooling effects among other lubrication methods. Experimental data demonstrate that NMQL compared with dry cutting, flood cutting and pure MQL, the specific cutting energy is reduced by 31.3%, 19.13%, and 17.37%, respectively, and the cutting energy performance is significantly improved. The maximum error of the SCE prediction model is 17.5%, and the prediction results align well with the experimental findings. This article offers fresh insights for advancing machining theory and exploring sustainable green machining of nickel-based alloys.