摘要 : Along with the explosive utilization of intelligent and bionic robotics, the rise of somatosensory system with excellent flexibility and multiple biological sensing characteristic emerges as a substantial crux of this domain. Herein, we propose a flexible high-performance multi-mode sensor for real-time proximity–pressure–temperature perception based on a monolithic sensing unit with fingerprint-like hierarchical architecture. The monolithic sensing unit, primarily constituted by a double-permeable ionic liquids/Multi-walled nanotubes conductive network, demonstrates dual-functionality in detecting pressure and temperature. Making use of the further synergy of rational topographical architecture engineering and feasible decoupling algorithm construction, extraordinary progress in sensing performances for both pressure and temperature are attained with negligible mutual interferences. Additionally, the sensor is capable of switching to touchless mode to detect objects at distance up to 200 mm, validating its remarkable proximity sensing ability. The multifunctional nature of sensor is further substantiated through its integration with a robotic hand, highlighting its practical applicability in advanced robotic systems.

摘要 : Pipeline inspection technologies have become frontier topics. This paper proposes a method for pipeline inspection by using a self-propelled robot fish. To investigate the hydrodynamic performance of robot fish in pipelines environment, a series of numerical simulation and experimental test are performed. A continuous pipeline model which contains 4 types of different pipeline segments is established to study the hydrodynamic coefficients and flow field distribution of robot fish at different oscillating frequencies f and pipelines.The self-propelled speed test experiments are conducted to further reveal the propulsion performance of robot fish in different pipelines. The results indicate that the self-propelled speed, hydrodynamic coefficients, propulsive efficiency, and swimming stability of the robot fish change with the size and shape of the pipelines. The effect of pipelines on the propulsive performance of robot fish occurs when the diameter of the pipeline decreases to a certain value. Square pipeline with the same feature size as circular pipeline has the smaller effect on the propulsive performance of robot fish. Moreover, changing the oscillating frequency f can obtain different hydrodynamic performance.

摘要 : Roadheader is the core equipment of roadway excavation. Affected by the sudden change of cutting load and complex roadway floor conditions, the position pose of the roadheader is very prone to deviation. Usually, the roadheader's position pose deviation is corrected by controlling its supporting and tracking unit. However, this will greatly reduce the roadway excavation efficiency in case of a small position pose deviation. Therefore, a new scheme for the automatic compensation of position pose deviation is proposed by controlling the cutting boom motion. Firstly, the kinematics model of cutting boom is established, and the pitch and yaw position pose deviation compensation models are constructed. To improve the control accuracy of cutting boom motion, the reduced-order active disturbance rejection controller is designed. Then, the compensation control experiments of pitch and yaw position pose deviation are carried out based on the position pose compensation experiment system. The results show that the maximum errors of cutting trajectory after compensation meet the requirements of roadway cross-section quality, proving the effectiveness of the proposed position pose compensation control scheme.

摘要 : Real industrial scenarios struggle with the issues of a limited number of labeled samples and difficulty in accessing, which results in deep learning-based fault diagnosis models having poor generalization capabilities and decreased diagnostic accuracy. To address this problem, a semi-supervised prototype enhancement network (SSPENet) is proposed for rolling bearing fault diagnosis in this study. Firstly, a dual pooling attention residual network is proposed to be used in the feature extraction module. The goal is to efficiently extract the hidden features within rolling bearings, thus enabling the accurate classification of different sample categories. Subsequently, the Hungarian algorithm is utilized to design a strategic approach to update prototypes with pseudo-labels, which achieves the effect of augmenting prototypes by accurately adjusting the prototype position of each class of limited labeled samples through unlabeled samples, to improve the discriminative ability of the network model for fault classes. Finally, validation and experimental analysis are carried out on two bearing datasets, which achieve the average diagnostic accuracy of the proposed model to be above 90 % for both 1-shot and 2-shot cases, obtaining more satisfactory diagnostic results.

摘要 : Deep transfer learning is an effective method for unsupervised fault diagnosis of rolling bearings. In some works, the pseudo-label of target domain prediction is used to improve the ability of target domain prediction in transfer learning. However, its validity depends on the quality of pseudo-label generated by the network itself, which is easy to cause the misclassification of the samples. Aiming to this, a dual sample screening (DSS) method based on the information of predicted label changes is proposed in the article, and it is applied to the fault diagnosis of rolling bearings with variable working conditions. DSS combines pre-screening and real-time screening and uses the continuous output of prediction label change information in the training process to improve the network training. It owes to eliminating part of the target domain samples with prediction errors in the stage of network training with pseudo-label. The proposed method improves the stability of the pseudo-label involved in the training and alleviates the negative effects caused by the pseudo-label. The experimental results on Paderborn University dataset show that, compare with the deep transfer learning fault diagnosis method based on pseudo-label cross-entropy, the average diagnostic accuracy of the six transfer tasks using DSS is increased by 5.97%, which effectively improves the fault diagnosis accuracy of rolling bearings. Deep transfer learning is an effective method for unsupervised fault diagnosis of rolling bearings. In some works, the pseudo-label of target domain prediction is used to improve the ability of target domain prediction in transfer learning. However, its validity depends on the quality of pseudo-label generated by the network itself, which is easy to cause the misclassification of the samples. Aiming to this, a dual sample screening (DSS) method based on the information of predicted label changes is proposed in the article, and it is applied to the fault diagnosis of rolling bearings with variable working conditions. DSS combines pre-screening and real-time screening and uses the continuous output of prediction label change information in the training process to improve the network training. It owes to eliminating part of the target domain samples with prediction errors in the stage of network training with pseudo-label. The proposed method improves the stability of the pseudo-label involved in the training and alleviates the negative effects caused by the pseudo-label. The experimental results on Paderborn University dataset show that, compare with the deep transfer learning fault diagnosis method based on pseudo-label cross-entropy, the average diagnostic accuracy of the six transfer tasks using DSS is increased by 5.97%, which effectively improves the fault diagnosis accuracy of rolling bearings.

摘要 : Step error is the machining error between adjacent cutter location points in the feeding direction. In order to improve the computational efficiency, a hybrid particle swarm optimization method (HPSO) combining genetic algorithm (GA) and simulated annealing (SA) algorithm is proposed. The mapping relationship between local cutter contact (CC) curve in step error calculation and the particle search range and the fitness calculation model are established. The maximum fitness value is taken as step error. The chaotic initialization population is carried out by the Tent mapping. Two nonlinear control methods based on the Sigmoid function and the numbers of iterations are proposed for inertia weight and learning factors, respectively. Combined with the above optimizations, an improved particle swarm optimization algorithm (IPSO) is proposed algorithm is formed. Based on IPSO, the crossover and mutation strategies of GA are used to increase particle diversity, and then, Metropolis criterion from SA is applied to the particle selection; the improved crossover and mutation particle swarm optimization algorithm (ICMPSO) is formed. IPSO is used for Elite particles with higher fitness values to enhance the convergence speed. The other Ordinary particles employ ICMPSO to improve global search capability. The combination of IPSO and ICMPSO forms a whole hybrid particle swarm optimization (HPSO) method. All the proposed algorithms are implemented, and two typical free-form surfaces are taken as examples to calculate step errors. The calculation results show that the tool path generation time of the proposed method is lower than that of the geometric iterative algorithm and the standard particle swarm optimization algorithm, which verifies the feasibility and effectiveness.

摘要 : In this paper, a response band-based method for time-dependent reliability-based robust design optimization is proposed. The proposed method provides a novel alternative framework, consist of a two-step transformation stage and a solving stage, to solve the time-dependent reliability-based robust design optimization problem. The original time-dependent reliability-based robust design optimization problem is transformed into an equivalent deterministic robust design optimization problem in the transformation stage, and the equivalent problem is settled in the solving stage. In the transformation stage, the dynamic modal decomposition technique and the kriging technique are combined to overcome the problem that there is no standard for both time division and observation sampling in the commonly used transformation methods. In the solving stage, an approach for constructing the response band of the objective function is presented, which significantly reduces the computational consumption of the variation evaluation of the objective function. Five cases are employed to verify the effectiveness of the proposed method. In this paper, a response band-based method for time-dependent reliability-based robust design optimization is proposed. The proposed method provides a novel alternative framework, consist of a two-step transformation stage and a solving stage, to solve the time-dependent reliability-based robust design optimization problem. The original time-dependent reliability-based robust design optimization problem is transformed into an equivalent deterministic robust design optimization problem in the transformation stage, and the equivalent problem is settled in the solving stage. In the transformation stage, the dynamic modal decomposition technique and the kriging technique are combined to overcome the problem that there is no standard for both time division and observation sampling in the commonly used transformation methods. In the solving stage, an approach for constructing the response band of the objective function is presented, which significantly reduces the computational consumption of the variation evaluation of the objective function. Five cases are employed to verify the effectiveness of the proposed method.

摘要 : Owing to the advantages of monolithic structure and little need for assembling, compliant guiding mechanisms appear to be an effective solution for decoupling multi-freedom precision motions but are still prone to geometric nonlinearities of parasitic error and stiffening effect for large strokes. This paper proposes a coiled L-shape compliant guiding mechanism featuring millimeter-scale strokes with a compact structure, constant stiffness, and minimized parasitic error. The coiled compliant guiding mechanism is formed by convolving L-shape flexure beams in a zigzag configuration with decoupled XY motions achieved. Its geometrically nonlinear parasitic error, variation in stiffness, and primary vibration are captured by using a dynamic beam constraint model (DBCM). It is theoretically, numerically, and experimentally found, by comparing with double parallel guiding mechanisms, that the kinetostatic and dynamic behaviors of the coiled L-shape compliant mechanism are nearly independent on the applied force within intermediate-deformation ranges. Such a weak geometric nonlinearity with the minimized influence of axially loaded stiffening and kinematics-arching effects is much different from the double parallel guiding mechanisms. The obtained results indicate that large strokes with constant stiffness and invariable resonance frequency can be realized, which also allows small parasitic errors.

摘要 : TC4 titanium alloy is utilized in aerospace and a wide range of other applications due to its high strength and corrosion resistance, but its poor thermal conductivity and high ductility introduce challenges in machining. In this paper, firstly, a mathematical model of workpiece surface morphology under tilted ultrasonic elliptical vibration cutting (TUEVC) was established based on the tip trajectory and material removal mechanism, and the variation of workpiece surface morphology was investigated at different tool tilt angles θ, and it was found that the tilt θ in a certain range could significantly reduce the surface residual height compared with that of ordinary ultrasonic elliptical vibration cutting (UEVC). Second, the TUEVC experiment of TC4 titanium alloy was carried out to comparatively analyze the changes in the surface morphology, surface profile, and surface roughness of the workpiece under different tilt θ, the effect of each machining parameter (cutting speed, feed, and ultrasonic amplitude) on surface roughness was explored. The experimental results indicate that as the tilt θ changes from 0° to 90° throughout the process, the workpiece surface morphology flatness decreases and then increases. When the tilt angle θ is 45°, workpiece cutting surface roughness is minimized ( Sa = 0.157), compared with the ordinary ultrasonic elliptical vibration cutting roughness is reduced by 47.6 % maximum. Both the surface morphology flatness and the surface roughness of the workpiece are at their smallest, whereas the theoretical profile curve and cutting surface profile curve are at their most consistent. Under the same machining parameters, TUEVC can reduce the surface roughness more effectively compared with UEVC, this technique reduces surface roughness by 16 %, 23 %, and 26 % at maximum for different cutting speeds, feeds, and ultrasonic amplitudes, respectively.