Ionic touch panels are regarded as a key platform for future human-computer interaction and meta-universe due to their stretchable, transparent and skin-fitting properties. Inspired by the fact that human skin relies on ionic current to sense contact position information, we have investigated an ionogel based closed-loop electrical system that also converts contact into ionic current to form a self-powered single-layer ionic touch panel. Benefiting from the slowed charge transfer dynamics, the positive feedback coupling of the electrical double layer, and the high-density charge characteristics, the device generates an approximately steady-state electrical signal when touched. It is clearly different from the pulsed electrical phenomenon of conventional contact electrification devices. When a finger touches the touch panel, the voltage/current signal amplitude at each corner electrode of the ionogel has been proven to express the touch position. The continuity of the electrical signal ensures high-resolution recognition of the touch trajectory without the need for further contact separation. With the advantages of good transparency, large stretchability, self-power, single-layer structure, fast response and high resolution, we expect this emerging ionic touch panel to be an ideal candidate for a variety of human-computer interaction applications.

Human skin transmits tactile information via ionic currents, with rapidly adapting Pacinian corpuscles capable of precisely responding to high-frequency dynamic signals in the 50–500Hz range, supporting fine manipulation and environmental perception. Although ion-piezoelectric/ion-triboelectric artificial skins can directly generate dynamic ionic currents in response to mechanical stimuli, under high-frequency deformation, viscoelastic energy dissipation in the polymer and ionic migration lag limit the detection frequency of self-powered ionic sensors typically below 100Hz. This work proposes a strategy to effectively reduce viscoelastic energy dissipation and interfacial friction, while simultaneously enhancing ion conductivity, by modulating polymer chain entanglement and introducing appropriate chemical crosslinking. This strategy enables the material to exhibit low mechanical hysteresis (~1.7% hysteresis at 30% compressive strain) and high elasticity under large deformation, thus extending the frequency detection range of the self-powered ion-electronic sensor to 800Hz. The resulting device achieves high sensitivity (0.01mm vibration/0.012Pa acoustic pressure), excellent stability (100,000 cycles at 40Hz), and self-powered capability, successfully achieving acoustic detection and 99.12% accuracy in machine learning-based speech recognition.

Triboiontronic is an emerging science, due to the rapid generation and dissipation of charge, current triboiontronic devices output transient signals, which are suitable only for dynamic forces monitoring. Yet many fields also require monitoring of sustained contact forces, demanding devices that output long-term, precise, steady-state signals. However, the core challenge of triboiontronic devices achieving stable static signals lies in maintaining the slow and continuous replenishment of charge at the device interface. Here, bi-phase ionogels are obtained by co-dissolving thermoplastic polyurethane(TPU) and ionic liquids in a solvent. The polymer-rich phase mimics mammalian Merkel cell nano-channels, and the ionic liquid-rich phase provides differentially migrating anions and cations to establish a dynamic EDL at the contact interface in response to contact pressure. Due to the slowed-down and sustained charge transfer kinetics of the nano-channels and the high-density charge nature of triboiontronic, the devices generate near steady-state electrical signals at a fixed contact pressure, complementing other recent triboiontronic approaches. The slow relaxation pattern of the electrical signal under long-term static contact pressure (the voltage attenuation rate is only 1.53% in 120 s under static force stimulation) is very similar to the slow decrease in skin sensitivity under prolonged pressing, which is expected to have great prospects in the fields of tactile sensing, new energy, brain-like neural bionics, etc.

Developing simple device configurations to satisfy independent thermal and force sensing is critical for improving the recognition and manipulation capabilities of robotic hands on objects, but it remains a challenge. In this paper, through the composition adjustment of polyvinyl alcohol, KOH, nano -silica, glycerin, and the microporous structure design, the prepared ionic hydrogel sensor has promising comprehensive performance. It shows thermal sensitivity (-0.47 % degrees C-1, 0-20 degrees C; -0.24 % degrees C-1, 20-50 degrees C), pressure sensitivity (1 kPa-1, 0-300 kPa), pressure detection limit of 1 Pa, and mechanical durability (1000 compression fatigue cycles at 100 kPa). More importantly, the pressure -sensitive and thermal -sensitive properties hardly interfere with each other due to the ionic hydrogel network's synergy of excellent water retention, anti -freezing properties and heat insulation ability. The multifunctional tactile sensor is integrated into a robot hand to obtain force and thermal information of the measured object, and combined with machine learning algorithms to realize object recognition. The proposed sensing scheme has been applied to 6 kinds of fruit and vegetable classification tasks in a cold chain logistics scenario, showing a classification accuracy of 95 %.

Vibration in the environment usually shows uncertainty with random changes in frequency and sudden accel-eration impact. However, it is a huge challenge for existing vibration energy harvesters and sensors to work in random and even extreme vibration environments due to their rigid multi-component architecture with me-chanical mismatches. Here, we propose a general design of vibration transduction with broadband response and high mechanical robustness that mimics the biological musculoskeletal system. Such design is based on a ten-segrity structure, consisting of a rigid frame and soft strings, combined with triboelectric nanogenerator (TENG). The Tensegrity-inspired triboelectric nanogenerator is of broadband (0-200 Hz) frequency response and exhibits excellent impact resistance under high g acceleration impacts (105 g level). The device can still work normally after undergoing some structural damage. It has been successfully applied to the aeolian vibration monitoring of transmission lines and shows more reliable performance than commercial sensors when suffering the hail impact. Such tensegrity structure design has great potential in high-performance vibration monitoring in the industrial environment.

With the advancement of urbanization and the rapid development of the electric power industry, the safe operation of the electric power grid has become more and more important. Most cable trenches in cities are built underground, and under the harsh environment of heavy rainfall and flooding, rainwater will flow into the wells. The large amount of water in the cable trench can lead to cable short-circuit, cause cable burst and other problems, and damage the normal operation of the power grid. Therefore, real-time monitoring of water levels in cable trenches and substations is crucial to protect them from the threat of flooding. This paper introduces an iontronic pressure sensor for the water level surveillance in cable trenches and substations. The sensor is a double electric layer (EDL) based capacitive pressure sensor and has a minimum pressure monitoring value of 25 Pa and a sensitivity of S = 0.85 kPa−1 within the range of 0–450 kPa. It also has a quick response time of 822 ms for applied pressure and 424 ms for removed pressure. Additionally, it has shown stability for 2000 cycles of compression and release. We use the sensor for water level monitoring. The sensor can monitor different water levels very well and the sensor responds quickly and steadily even if the water level changes by only 1 cm.

In this work, a static vibration test bench for diagnosing the abnormal wear states of the Tunnel Boring Machine (TBM) disc-cutters is built, and combined with the Long and Short-Term Memory (LSTM) algorithm model, the abnormal wear states are intelligently diagnosed. The test bench can apply vibration to the disc-cutters, so that the disc cutters in abnormal wear states can produce differentiated vibration response characteristics. After collecting the vibration signals by the sensor and processing the vibration features by the LSTM model, the accuracy of the feature learning model in diagnosing abnormal wear states of cutters exceeds 99.8%. The proposed test bench can be used as a key tool for studying the vibration characteristics of disc-cutters. The abnormal wear diagnosis method of disc cutters can be used in any complex geological conditions, exhibiting high reliability.

Building prosthetics indistinguishable from human limbs to accurately receive and transmit sensory information to users not only promises to radically improve the lives of amputees, but also shows potential in a range of robotic applications. Currently, a mainstream approach is to embed electrical or optical sensors with force/thermal sensing functions on the surface or inside of prosthetic fingers. Compared with electrical sensing technologies, tactile sensors based on stretchable optical waveguides have the advantages of easy fabrication, chemical safety, environmental stability, and compatibility with prosthetic structural materials. However, so far, research has mainly focused on the perception of finger joint motion or external press, and there is still a lack of study on optical sensors with fingertip tactile capabilities (such as texture, hardness, slip detection, etc.). Here we report a 3D printing prosthetic finger with flexible chromatic optical waveguides implanted at the fingertip. The finger achieves distributed displacement/force sensing detection, and exhibits high sensitivity, fast response and good stability. The finger can be used to conduct active sensory experiments, and the detection parameters include object contour, hardness, slip direction and speed, temperature, etc. Finally, exploratory research on identifying and manipulating objects is carried out with this finger. The developed prosthetic finger can artificially recreate touch perception and realize complex functions such as note-writing analysis and braille recognition.

As the core component of the Tunnel Boring Machine (TBM) rock-breaking function, disc -cutters directly affect the service life and construction efficiency of the TBM. Accurately predicting the wear status of disc -cutters is critical to efficient reel replacement decisions. However, due to the complex environment of cutters, both manual inspection and traditional sensor detection are subject to strong interference, which reduces efficiency and effectiveness. To solve this problem, a combined method LSTM-CNN based on long short-term memory (LSTM) network and convolutional neural network (CNN) is proposed, which predicts the wear status of the disc-cutters based on vibration dataset. The high-sensitivity vibration sensor is used to collect signals in the rock-breaking test, and then the deep learning model is used to eliminate interference signals to extract effective features, and the prediction of the three kinds of disccutters wear status (normal, uniform -wear failure and angled wear failure) is realized. Comparing the LSTM-CNN model with support vector machine (SVM) and traditional LSTM, the results show that the LSTM-CNN outperforms the other two models.

Carrying out status monitoring and fault-diagnosis research on cutter-wear status is of great significance for real-time understanding of the health status of Tunnel Boring Machine (TBM) equipment and reducing downtime losses. In this work, we proposed a new method to diagnose the abnormal wear state of the disc cutter by using brain-like artificial intelligence to process and analyze the vibration signal in the dynamic contact between the disc cutter and the rock. This method is mainly aimed at realizing the diagnosis and identification of the abnormal wear state of the cutter, and is not aimed at the accurate measurement of the wear amount. The author believes that when the TBM is operating at full power, the cutting forces are very high and the rock is successively broken, resulting in a complex circumstance, which is inconvenient to vibration signal acquisition and transmission. If only a small thrust is applied, to make the cutters just contact with the rock (less penetration), then the cutters will run more smoothly and suffer less environmental interference, which would be beneficial to apply the method proposed in this paper to detect the state of the cutters. A specific example was to use the frequency-domain characteristics of the periodic vibration waveform during the contact between the cutter and the granite to identify the wear status (including normal wear state, wear failure state, angled wear failure state) of the disc cutter through the artificial neural network, and the diagnosis accuracy rate is 90%.

Flexible sensors with the ability to precisely detect the full range of tiny strain (less than 0.1%), small strain (within 1%), and large strain (≈50%) are in significant demand to satisfy the requirements for electronic skin applications. More importantly, the sensor performance is required to be accurate and reliable when operating in some unconstrained environments, such as excessive extension, high bending, torsion, and scratching impact. However, it remains challenging to meet all these requirements simultaneously in a single strain sensor. Herein, an ultrathin composite film composed of reduced oxide (rGO) and carbon tube (CNT) is prepared, and then transferred onto a modified elastomer polydimethylsiloxane surface that forms strong hydrogen bond interaction with the film. The as‐fabricated sensor achieves wide range and high sensitivity (gauge factor (GF) ≈ 105, 160, and 310 in the strain regions of 0–25%, 25–40%, and 40–50%, respectively). More importantly, the proposed strain sensor performs mechanical robustness, low hysteresis, scratch resistance due to the effective improvement of interfacial slipping and delamination. The sensor can be used to monitor human physiological information, including pulse waveforms in a variety of wrist postures and acoustic vibration signal (≈7 kHz).

以TBM再制造为对象,阐述了硬岩掘进机再制造的总体流程和关键技术,突出再制造设计和优化升级的必要性,对TBM按系统分类进行部件检测、拆解、修复和测试,并对过程中的注意事项进行探讨,强调了再制造设备售后维保及全生命周期服务的重要性,并对TBM再制造未来前景进行畅想和展望.

In this study, a visualized real-time monitoring system was established to monitor the working conditions of the slurry chamber and collect rotation situations of the cutter head, wear conditions of the disc cutters, image information of the excavated strata and flow characteristics of the slurry. The operating condition of the front-end equipment is shown on the upper computer, and the signal collected by the camera is transmitted to the video acquisition system in real-time. Hence, this paper presents solutions to the system with an emphasis on the system structure, hardware, and software design, and verification. The system verification was conducted through a field test in a project site, where the system could reliably monitor the slurry chamber.

对盾构螺旋输送机后闸门紧急关闭系统的工程应用,同时设计了一套液压控制系统以实现后闸门的紧急关闭功能,并针对不同工况下的工作原理进行了阐述,同时就其关键部件蓄能器所需的容积进行了选型计算,为今后类似系统的设计提供参考.

盾构机是集光、机、电、液、传感、信息技术于一体的高精尖机械设备,被称为"工程机械之王".盾构机的调试复杂而又困难,需要专业的技术知识做储备.调试人员作为专业的技术人员,其培养及管理都是行业的难题.本文提出人才培养及管理的创新思路,注重"轮岗"和"导师制"的模式,供行业技术人员参考.

本文结合施工实例,针对超大直径盾构机工地组装所面临的重点、难点问题进行分析,重点研究了超大直径盾构机工地组装过程中超大尺寸、超重及关键部件的组装关键技术,同时介绍了组装的工序、注意事项等.本文可为今后类似工程提供相关的技术指导及参考,具有较大的借鉴意义.

针对盾构施工中注浆浆液配比不合理、二次注浆、长时间停机等原因造成盾体表面被固结砂浆包裹之后引发掘进姿态超限、开挖面增大、地表沉降、管片破损等问题,通过城市隧道施工为依托,结合现场施工实际情况,详细介绍了盾构盾壳被固结砂浆包裹的处理措施及注意事项,为类似施工问题处理提供参考.

针对WinForm中GDI做动画效果占用大量系统资源的弊端,提出采用WPF技术,直观形象地在监控界面上显示泥水环流效果.

文章分析了目前国内混凝土喷射应用的现状及存在的不足，介绍了一种满足市场需求的泵式混凝土湿喷机的工作原理，提出了基于PLC和触摸屏技术的控制方案，并对控制系统软件、硬件的设计进行了阐述。该泵式湿喷机经过多地的工业试验及应用，体现了自动化程度高、喷射效率高、可靠性高以及回弹率低等优势，具有很大的推广应用前景。

泥浆泵是泥水平衡盾构重要的输送设备,其选型设计对泥水环流系统的使用寿命和稳定性有着很大的影响.以6m地铁泥水平衡盾构为例,对泥浆泵选型常用参数和公式进行了汇总,对泥浆泵设计常见问题进行了阐述,为其他泥水平衡盾构泥浆泵选型与设计提供参考与借鉴.