Natural Orifice Transluminal Endoscopic Surgery (NOTES) imposes exceptionally high requirements on the operational precision and tissue safety of flexible robots. To address the prevalent "force-position conflict" during complex interactions, this paper proposes a bi-level master-follower shared control framework based on a dynamic virtual fixture (VF) and adaptive model predictive control (MPC). In this framework, the upper level utilizes dynamic VF to guide operator intent and restrict motion in non-safe regions, while the lower level incorporates an adaptive MPC controller. The core innovation lies in an adaptive weight scheduling mechanism that dynamically adjusts the position and force tracking weights by perceiving the terminal contact force state in real time. Furthermore, the rigorous mathematical foundation of the proposed framework is established by analyzing closed-loop nominal stability, recursive feasibility under practical environmental bounds, and the passivity of the haptic interaction loop. Trajectory tracking and ex vivo surgical simulation experiments, benchmarked under a standardized Independent Peak-Performance Tuning Protocol, demonstrate that the proposed method strictly confines the contact force within the 0.5 N safety threshold with zero force overload, while simultaneously suppressing the trajectory RMSE to within 1.32 mm. This study effectively breaks through the bottleneck of force-position synergistic control in unstructured environments, significantly enhancing the accuracy of fine operations while absolutely guaranteeing the physical safety of soft tissues.

Flexible surgical robots are promising for natural orifice transluminal endoscopic surgery (NOTES) owing to their compact structure and high maneuverability in narrow, curved luminal pathways. However, limited environmental and tactile awareness under visually constrained conditions hinders tissue contact regulation and lesion-mimicking palpation. Existing sensing approaches mainly emphasize shape reconstruction, posture estimation, or distal force measurement, while conformable, low-crosstalk tactile sensors for distributed surface contact perception remain insufficiently developed. Here, we present a low-crosstalk piezoresistive tactile sensor array fabricated by hybrid laser 3D printing and in situ laser-induced graphene (LIG) integration. Spatially discrete sensing units connected by curved support beams enable conformal attachment to both the distal end and sidewall of a flexible robot while suppressing interchannel mechanical coupling. The array achieved a mean crosstalk isolation of 29dB. Benefiting from the laser-cured neodymium–iron–boron/polydimethylsiloxane composite layer and porous LIG network, each unit exhibited a detection limit of 30Pa, an initial sensitivity of 1.36kPa−1, a response time of 74ms, and stable output over 1000 loading/unloading cycles. Combined with multichannel acquisition and a two-dimensional convolutional neural network, the system classified materials with different mechanical properties with an accuracy of 98.23%. After integration with a flexible robotic platform, it enabled contact monitoring, tactile-feedback-assisted navigation, and lesion-mimicking palpation in NOTES-related experiments. These results demonstrate a manufacturable and integrable strategy for distributed, low-crosstalk tactile perception in flexible surgical robots.

Structural static testing is paramount for validating the structural integrity of critical aerospace components. However, conventional test rigs are often constrained to fixed loading axes and frequently induce parasitic torques. Accurate reproduction of aero-engine pylon flight loads therefore requires a mechanism that combines omnidirectional vector loading, high stiffness, and efficient force transmission. Achieving these coupled requirements is primarily a geometric synthesis problem, yet the associated workspace, stiffness, and load–capacity indices are nonlinear, mutually coupled, and expensive to evaluate over dense pose samples. To address this optimization bottleneck, this work develops a task-specific 6-UPS loading mechanism and a bio-inspired sensitivity-weighted NSGA-II algorithm for its geometric synthesis. Inspired by gene/locus-specific heterogeneity in biological evolution, the algorithm assigns variable-wise search intensities according to design-variable sensitivities, which are estimated using Multivariate Adaptive Regression Splines (MARS). In this way, influential design genes receive stronger local exploitation, whereas less sensitive ones retain broader exploration. Numerical simulations demonstrate that the proposed approach reduces computation time from about 30 h to 3 h relative to direct optimization with the baseline NSGA-II, while simultaneously improving workspace, stiffness, and load-carrying capacity. A hybrid physical prototype was further tested under 240 loaded pose conditions; the system maintained force magnitude errors below 0.64% (63.42 N) and directional deviations below 1.15°. These results support the efficacy of the proposed bio-inspired optimization-based design methodology for high-fidelity static testing of aero-engine pylons under the adopted hybrid setup.

Real-time morphological perception and precise end force feedback prediction of surgical robots constitute critical technical elements for ensuring safety and efficacy in complex interventional procedures such as Endoscopic Retrograde Cholangiopancreatography (ERCP). In this paper, we design a miniature flexible surgical robot (FSR) with a nested spring structure and proposed a physics-informed deep learning approach to simultaneously predict both the FSR's shape and 2D contact forces at its end-effector. The physical constraints were derived from a quasi-static model of the FSR, which is capable of characterizing persistent environmental interactions. Our method eliminates the need for end-effector sensors, not only ensuring high accuracy in both shape and contact force predictions but also maintaining consistent predictive performance under continuous environmental interactions. Experimental validation of the method revealed a high consistency between predicted values and reference data, achieving a 34.97% improvement in computational speed and a maximum prediction accuracy enhancement of 71.64% compared to conventional LSTM approaches.

For wearable devices, minimizing the number of actuators is critical to reducing weight and cost. Commonly, an ankle exoskeleton requires two or more actuators to assist bilateral ankle joints. In this work, we propose a novel underactuated mechanism for ankle exoskeletons to assist bilateral ankle joints with a single actuator. The underactuated mechanism consists of rollers and cables connecting the two ankle joints and the actuator. The cable transmits the actuator force to the ankle joint only when the dorsiflexion angle exceeds a specified value. During walking, the underactuated mechanism alternately provides plantarflexion torque to the left and right ankle joints to assist push-off. The mathematical model of the underactuated mechanism is built and the design parameters are optimized. A series elastic actuator (SEA) is developed to generate the required forces on the cables. In addition, an assistive strategy based on gait recognition is proposed to automatically adjust assistance under different walking speeds. A series of experiments are conducted on an ankle exoskeleton, and the results verify the feasibility of the underactuated mechanism and the assistive strategy.

Supernumerary robotic limbs (SRLs) is a new wearable device that augments the wearer’s physical capabilities by providing additional robotic limbs. During the human-robot collaborative operation, SRLs require an efficient force interaction strategy that can dynamically adjust the output force of robotic limbs according to the wearer’s intention to provide reliable assistance, which is a persistent problem in human-robot interaction. We propose a novel sEMG-based force interaction strategy for SRLs to assist in installing sheets. It uses a myoelectric armband to collect the sEMG signals of the wearer while performing the task, and a force estimation model based on BP neural network was developed to estimate the output force of the wearer. The estimation comparison experiment shows that the RMSE, MAPE, and R2 of the model are 1.16, 4

This article proposes a flexible surgical robot featuring strong magnetic steering achieved by a hemispherical magnet array actuation, and high‐accuracy ultrasonic position sensing achieved by a beacon total focusing method (b‐TFM). The hemispherical magnet array with magnetic focusing is described and its array parameters are optimized through finite element analysis to increase the magnetic field for actuation. The magnetic field strength at 100 mm for the array with the same mass as the cylindrical magnet is about 1.8 times higher than that of the cylindrical magnet. Using the magnet array actuation, the flexible robot exhibits the capability of agile steering to navigate along a predefined trajectory. In addition, a 1 mm × 1 mm lead zirconate titanate (PZT) patch is embedded into the tip of the flexible robot as a beacon for b‐TFM ultrasonic imaging to detect the position of the robot. Therefore, the entire navigation process can be executed under the supervision of the ultrasonic position sensing system, and the maximum error is 0.8 mm when the steering radius is 100 mm.

The laser power bed fusion (LPBF) forming process introduces heat accumulation and variations in powder layer thickness, which can destabilize the melt track and reduce surface quality. This phenomenon is especially more serious in the early printing stage. To tackle this stability problem, we proposed a novel approach optimizing process parameters on a layer-specific basis. At first, a numerical database was constructed through a set of numerical simulations. Then, a neural network prediction model was trained based on the database. Finally, this prediction model was embedded into a genetic algorithm for layer-based prediction. To verify the prediction results on processing parameters and calibrate the prediction model, physical experiments were prepared. The developed model consistently exhibited relative errors mostly within 6%. It is noteworthy that the relative errors between the numerical simulation results and the expected values were only 0.77% in width and 1.11% in depth. Printing optimization test was applied for an LPBF machine with a Invar alloy powder. The proposed method yielded positive results in both numerical simulations and the printing test. It can be further adopted for new material printing parameter optimization due to an efficient printing stability in the early-stage for LPBF process.

The impacts of subsurface species of catalysts on reaction processes are still under debate, largely due to a lack of characterization methods for distinguishing these species from the surface species and the bulk. By using 17O solid-state nuclear magnetic resonance (NMR) spectroscopy, which can distinguish subsurface oxygen ions in CeO2 (111) nanorods, we explore the effects of subsurface species of oxides in CO oxidation reactions. The intensities of the 17O NMR signals due to surface and subsurface oxygen ions decrease after the introduction of CO into CeO2 nanorods, with a more significant decrease observed for the latter, confirming the participation of subsurface oxygen species. Density functional theory calculations show that the reaction involves subsurface oxygen ions filling the surface oxygen vacancies created by the direct contact of surface oxygen with CO. This new approach can be extended to the study of the role of oxygen species in other catalytic reactions.

Compared with traditional open surgery, robot-assisted minimally invasive surgery has the significant advantages of smaller incision, less bleeding and faster recovery, and has been widely used in the field of surgery. However, existing robot-assisted surgical systems usually suffer from the limited motion space and the lack of tactile sensing, which prevent them from correctly evaluating the interaction between surgical instruments and organs during surgery. Applying insufficient or excessive force to surgical instruments may result in failure of the surgical operation or organ damage. Therefore, in this paper a multifunctional robot-assisted surgical forceps with tactile sensor array is designed, which can be used as a palpation probe and a clamping forceps at the same time. Tactile sensor arrays each containing 32 sensor units are integrated on the upper and lower gripping surfaces of the forceps. The sensor is based on the principle of capacitive sensing with pole pitch variation. Each sensor unit has an area of only 1mm 2 , which has an accuracy of 0.1Mpa in the measurement range of 0-3 MPa. Finally, the multifunctional forceps is used to perform tumor palpation detection experiments as a probe and suture needle clamping detection experiments as the forceps to verify the feasibility of this forceps in minimally invasive surgery.

In order to meet the requirements of aircraft skin assembly quality, it is necessary to eliminate step differences around the seam of the skin. The skin seam location and step difference measurement before grinding are the key steps to determine the grinding accuracy. This paper proposes a point clouds processing algorithm of seam location and step difference measurement for grinding trajectory generation of the curved components. First, extract the boundary features of the preprocessed workpiece point clouds, and divide the point clouds into different regions; Second, recognize the boundary feature points, and construct the boundary line by fitting the boundary points; Finally, calculate the step difference based on the boundary line of both sides and generate the solution for grinding track generation based on the seam boundary position and the step difference. We use the line structured light based vision measurement platform to verify the proposed algorithm, and the results show that the measurement system and algorithm can achieve the seam boundary location of curved components accurately. The research work in this paper is extendable to applications in the machining of curved workpieces.

Purpose The objective of this study is to investigate the feasibility of using selective laser melting (SLM) process to print fine capillary wick porous structures for heat pipe applications and clarify the interrelations between the printing parameters and the structure functional performance to form guidelines for design and printing preparation. Design/methodology/approach A new toolpath-based construction method is adopted to prepare the printing of capillary wick with fine pores in SLM process. This method uses physical melting toolpath profile with associated printing parameters to directly define slices and assemble them into a printing data model to ensure manufacturability and reduce precision loss of data model transformation in the printing preparation stage. The performance of the sample was characterised by a set of standard experiments and the relationship between the printing parameters and the structure performance is modeled. Findings The results show that SLM-printed capillary wick porous structures exhibit better performance in terms of pore diameter and related permeability than that of structures formed using traditional sintering methods, generally 15 times greater. The print hatching space and infilling pattern have a critical impact on functional porosity and permeability. An empirical formula was obtained to describe this impact and can serve as a reference for the design and printing of capillary wicks in future applications. Originality/value This research proves the feasibility of using SLM process to printing functional capillary wicks in extremely fine pores with improved functional performance. It is the first time to reveal the relations among the pore shapes, printing parameters and functional performance. The research results can be used as a reference for heat pipe design and printing in future industrial applications.

Triply periodic minimal surface (TPMS) cellular structures of Ti6Al4V with theoretically calculated relative densities ranging from 4% to 22.6% were designed using a toolpath-based construction method and fabricated by laser powder bed fusion, and their macrostructure, microstructure, and compression performance were investigated. The results indicated that the macrostructure was the same as that of TPMS structures designed using the traditional method. In contrast, the microstructures of the as-built samples and the samples after stress-relief annealing were slightly different from those of the traditional ones. Moreover, compression test results of the Schwarz-P structures showed that the compressive modulus was positively related to the calculated relative density, and a Gibson-Ashby model was established to quantitatively describe the relationship between the compressive modulus and theoretical relative density. The findings of this work show that the mechanical performance of a TPMS structure obtained using a toolpath-based construction design can be accurately predicted using geometric parameters or printing toolpaths. This will be helpful during the design stage.

Manufacturability analysis is a critical step before manufacturing to reduce costs and risks. It is used widely in conventional manufacturing (CM) processes. However, to the best of our knowledge, there is no natural method to evaluate the manufacturability of additive manufacturing (AM) processes that have more uncertainty-derived risks and costs than CM processes. A clear definition of the manufacturability of AM processes has not been established, and there is no standard to check whether a component is manufactured successfully by an AM process, particularly for porous complex components. This study introduces the development of a new machine learning-based method to solve the problem mentioned above. It is based on the statistical measurement of experimental samples. The proposed method can be used to perform the manufacturability analysis for periodic cellular structures printed by a selective laser melting (SLM) process. A novel definition of the manufacturability of the SLM-ed periodic cellular structure was proposed. Experimental results indicate that the developed learning model (ANN model) can achieve up to 94% classification accuracy and 96% prediction accuracy, which satisfies the application requirements of the AM industry. Moreover, the developed model can be adapted for the manufacturability analysis of different AM processes.

针对微电机质量检测水平低、故障识别困难等问题,设计一种基于声学特征的微电机故障诊断方法.通过声音采集装置获得微电机转动时的正常声音信号和三种故障信号;从声音信号中提取39维梅尔频率倒谱系数和短时能量,搭建一维卷积神经网络模型进行识别.将声音信号转化成语谱图,建立二维卷积神经网络模型并识别.利用多模型融合技术中的加权平均算法将两个模型融合,融合后模型的准确率为93.58％,比单个模型平均提高2.43％.

The microenvironment surrounding the metal clusters on a carrier produces a tremendous influence on its catalytic performance. In this work, the promotion effect of the zeolitic inner host on catalytic performance of encapsulated platinum nanoclusters is reported. In the reaction of phenylacetylene semihydrogenation to styrene, Pt@X-zeolite, where platinum nanoclusters are encapsulated into the inner microporosity of the X-zeolite, exhibits an ∼3.37 times increased turnover frequency and a much better selectivity of 87.6% in comparison to the referenced Pt/X-zeolite of 79.3% selectivity to styrene at the same reaction conditions, in which the platinum nanoclusters are located at the exterior of the zeolite. Meanwhile, the Pt@X-zeolite displays a higher stability after 10 cycles of the reaction. Through the detailed characteristics, the excellent performance of Pt@X-zeolite is mainly due to the promotion of the zeolitic framework on the encapsulated Pt clusters, resulting in "electron-deficient" Pt clusters, leading to a stronger interaction with the π* molecular orbitals of phenylacetylene and thus enhancing the activation and conversion of phenylacetylene. The zeolite cavity wrapped with encapsulated Pt clusters regulates the adsorption trend of phenylacetylene through the acetylene group on it, promotes the desorption of styrene, and strengthens its selectivity. Meanwhile, Pt@X-zeolite has an excellent stability through the zeolite framework, which protects the Pt species from being lost. This investigation reveals the importance of the zeolitic microenvironment on the catalytic performance of encapsulated metal species and deepens the cognition for this type of catalyst.

Current porous structure design methods in additive manufacturing (AM) lose accuracy in data model transformations along the processing chain and are difficult to consider manufacturability and post-processing issues. In addition, the design and printing preparation is costly due to large number of fine features and their related operations. To solve these problems with an aim to save time in design and printing preparation but ensure manufacturability and easy post-processing, this paper proposes an implicit design method using printing toolpaths to construct printable parametric porous structures. Experimental case studies demonstrated the feasibility, efficiency and application potential of the proposed method.

Purpose The manufacturability of extremely fine porous structures in the SLM process has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. The research objective is to find out the process limitation and key processing parameters for printing fine porous structures so as to give reference for design and manufacturing planning. Design/methodology/approach In metallic AM processes, the difficulty of geometric modeling and manufacturing of structures with pore sizes less than 350 μm exists. The manufacturability of porous structures in selective laser melting (SLM) has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. To solve this problem, a comprehensive experimental study was conducted to benchmark the manufacturability of the SLM process for extremely fine porous structures (less than 350 um and near a limitation of 100 um) and propose a manufacturing result evaluation method. Numerous porous structure samples were printed to help collect critical datasets for manufacturability analysis. Findings The results show that the SLM process can achieve an extreme fine feature with a diameter of 90 μm in stable process control, and the process parameters with their control strategies as well as the printing process planning have an important impact on the printing results. A statistical analysis reveals the implicit complex relations between the porous structure geometries and the SLM process parameter settings. Originality/value It is the first time to investigate the manufacturability of extremely fine porous structures of SLM. The method for manufacturability analysis and printing parameter control of fine porous structure are discussed.

针对微电机装配质量控制水平低、产线故障发现不及时且难以做出最佳决策等问题,提出一种基于卷积神经网络(CNN)的微电机装配故障诊断方法.该方法将实时采集的微电机装配过程质量特性数据绘制出控制图,采用数值转化为图像的数据预处理方法实现CNN对控制图异常模式的识别,最后通过控制图异常模式向故障映射的方法完成故障诊断.基于该方法开发了一套微电机装配故障诊断原型系统,可用于微电机装配过程的实时监控与故障诊断.

The significant passivation effect of the zeolitic framework on the catalytic performance of Pt clusters for dehydrogenation of propane to propylene is displayed. Pt/NaX shows 1100% enhanced TOFs and largely improved selectivity compared with Pt@NaX.