Octopuses can whip their soft arms with a characteristic “bend propagation” motion to capture prey with sensitive suckers. This relatively simple strategy provides models for robotic grasping, controllable with a small number of inputs, and a highly deformable arm with sensing capabilities. Here, we implemented an electronics-integrated soft octopus arm (E-SOAM) capable of reaching, sensing, grasping

Beamforming techniques cannot effectively separate the mixed field of stationary and rotating sound sources. The issue becomes even more challenging when strong out-of-focus sources, e.g., stationary sources with rotating beamforming, are present, leading to a smearing effect and false localization results. The current algorithms for separating multiple moving sound sources have problems such as weak

We propose a novel surrogate modelling approach to efficiently and accurately approximate the response of complex dynamical systems driven by time-varying exogenous excitations over extended time periods. Our approach, namely manifold nonlinear autoregressive modelling with exogenous input (mNARX), involves constructing a problem-specific exogenous input manifold that is optimal for constructing autoregressive

This work examines the active control of vehicle buffeting noise using a dielectric barrier discharge plasma actuator (DBD-PA). First, the buffeting noise characteristics of a cavity under different inflow wind speeds are established through a wind tunnel test, and the effectiveness of the buffeting noise measurement results is verified. Various types of actuator layouts are designed based on specific

Frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency, which has a fundamental role in improving the dynamic performance of resonant devices. Most research has been focused on a single mechanical frequency locking, but less research has been done on

Most fault identification methods based on deep learning rely on a large amount of data, and their effects are limited in the actual production environment. In the case of multiple classification, limited data and complex working conditions, the bearing fault identification is very difficult. In this paper, a novel method called ensemble transformer meta-learning (ETML) is proposed for bearing fault

Green function is the important component for ultrasonic time reversal based super resolution imaging methods. Time reversal with multiple signal classification (TR-MUSIC) and its phase-coherent form (PC-MUSIC) are two typical imaging methods. In this paper, the effect of Green function on the TR-MUSIC and PC-MUSIC has been investigated. And TFM has also been introduced as a comparison. Point spread

Human–robot collaboration (HRC) systems are intelligent systems that guide robots to collaborate with humans based on a cognitive understanding of human intention, ensuring safe, flexible, and efficient collaboration between humans and robots in shared workspaces. In industrial settings, the current methods for constructing a human digital twin model rely on motion capture devices that require personnel

In this paper, we propose a highly reliable and accurate collision estimator for robot manipulators working in human–robot collaborative environments, based on the Bayesian approach for practical uses. We assume the robot collision as a dynamic Markov process, not a static event, to reflect the transient behavior of mechanical collisions. Thus, the collision estimator can integrate the prior belief

The parasitic motion of microgripper jaws affect clamping accuracy. The parallelogram mechanism (PM) is often used as the last-stage of the multi-stage amplification mechanisms (AMs) because of its parallel clamping characteristics. However, the PM traditionally used in multi-stage AMs is asymmetrically stressed, which will affect its parallel clamping accuracy. Based on this, a PM with symmetrical

Effective bridge structural health monitoring systems offer a route to assist in the safe and economic operation of bridges, but have seen limited adoption onto the short and medium-span bridges which represent the majority of our bridge stock. This lack of adoption is in part due to the practical challenges of deploying bridge SHM systems in resource-constrained environments, especially where long-term

Latching control is a phase optimization control mechanism for improving the energy conversion efficiency of a wave energy converter (WEC). This paper, for the first time, extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed in this paper by integrating conventional tuned mass dampers (TMD) and

The forward computational modal analysis (CMA) of the motor stator assembly is gaining attention as a technical challenge in the area of motor noise. There is still no effective method to obtain accurate anisotropic material parameters (AMPs) required for CMA without prototype motors. In this paper, a novel forward calculation method (FCM) for calculating AMPs of the motor stator assembly is proposed

Elastic metasurfaces have shown remarkable ability in exotic wavefront manipulations based on the generalized Snell’s law (GSL), stimulating great promise for many potential applications, such as elastic wave signal processing, vibration control, and energy harvesting. However, elastic metasurfaces suffer from strong narrow-band confinement and dispersion modulation due to the inherent velocity-dispersive

A blade disk system plays a crucial role in the energy conversion efficiency of turbomachinery. Generally, a bladed disk is designed as a tuned system, which implies that the dynamic behaviors of all the blades are identical. In practice, there are some differences between the blades, known as mistuning, that can result in localized vibration. Thus, in this study, an approximate symplectic method developed

Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, the parametric model of cracked spur gear is established by simplifying the crack propagation path. LTCA method is used to calculate the time-varying meshing stiffness (TVMS) and transmission error (TE) of the cracked gear pair, and is verified by finite element (FE) method. The

We present an active control method for achieving non-collocated vibration absorption, implemented on a lumped mass system. The task of suppressing vibrations at a number of frequencies, at a target mass is recast into a problem of assigning zeros of the transfer function from the external excitation force to the position of the target mass. The controller design involves computing the controller parameters

Mixed-flow pump is an important machine for transporting fluids, which has the advantages of both centrifugal pump and axial flow pump, but it is prone to cavitation, endangering efficiency and safe operation. A 20?m?×?8.5?m high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were built for the study of cavitation signals. The detection of pressure pulsation signal

Health indicators (HI) are crucial in early fault alarm and degradation monitoring of mechanical failures. In recent years, many HIs are developed and reported, however, a constructed HI which is more sensitive to incipient defect, robust to varying operating conditions and is interpretable to characterize the degradation process is still remain to be studied. To address this issue, a more powerful

The ship lock system of the Three Gorges Hub(TGH) stands out as the most intricate navigation system for ships passing through dams, which is composed of the the world’s largest ship lock and ship lift. It operates with features like multi-dam configurations, multi-chambers, multi-operation modes and batch processing. This paper addresses the complex challenge of optimizing the lock schedule, ship

One of the most important technological challenges in the railway context nowadays is reducing the noise produced by the interaction between the train wheels and the track surface, that is the rolling noise. Introducing viscoelastic damping in the railway structure is one method to mitigate this rolling noise. In the paper, we investigate the impact of damping in the vibrational response of railway

In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in less structured environments, which remain beyond the reach of current robots. Prior works built reorientation systems assuming one or many of the following conditions: reorienting only specific objects with simple shapes, limited range of reorientation, slow or quasi-static manipulation

Automated building processes that enable efficient in situ resource utilization can facilitate construction in remote locations while simultaneously offering a carbon-reducing alternative to commonplace building practices. Toward these ends, we present a robotic construction pipeline that is capable of planning and building freeform stone walls and landscapes from highly heterogeneous local materials

The First Passage Time (FPT) of a given signal is the time required to reach a level Xf for the first time, when starting from a level X0. For a given set (X0,Xf), this time can be obtained by moving along the given signal and clocking the time on when passing through level X0, then off when passing through level Xf for the first time. For random signals, the FPT is a random variable. Several samples

Bearing skidding is a frequent phenomenon in rotating machinery, which causes equipment motion instability and bearing wear failure. Due to the difficulties in the manufacture of high-speed flexible rotor test rig, measurement of bearing motion parameters and complex structure of angular contact ball bearing (ACBB), which makes the research on double-piece inner ring ACBB skidding challenging. This

Deep learning-based remaining useful life (RUL) prediction methods have achieved great success due to their powerful capacity of feature representation especially when big data of condition monitoring is available. However, how to fuse multi-sensor information to facilitate RUL prediction accuracy remains a challenging problem due to the complex temporal and spatial dependencies within multi-sensor

In engineering practice, the operation of mechanical equipment under conditions of sharp speed variation can result in domain shift in the distribution of samples. Moreover, the presence of data deficiencies in engineering poses significant challenges for intelligent diagnostic techniques. To address these issues, this paper proposes a network called Hybrid Augmented network with Balance Domain Window

Convolutional neural network (CNN) is widely used in fault diagnosis of mechanical systems due to its powerful feature extraction and classification capabilities. However, the CNN is a typical black-box model, and the mechanism of CNN’s decision-making is not clear, which limits its application in high-reliability-required fault diagnosis scenarios. To tackle this issue, we propose a novel interpretable

Freeform surface inspection is vital in manufacturing-related processes, and the newly emerged five-axis continuous sweep scanning technology offers to be a powerful means for accurate inspection of freeform surfaces. However, at present, for an arbitrary freeform surface, it still heavily depends on human intervention in planning an interference-free sweep scanning path, wherein typically the surface

Future manufacturing will witness a shift in human-robot relationships toward collaboration, compassion, and coevolution. This will require seamless human-robot knowledge transfer. Differences in language and knowledge representation hinder the transfer of knowledge between humans and robots. Thus, a unified knowledge representation system that can be shared by humans and robots is essential. Driven

Residual features of large casting parts in small batch are random in distribution, size, and shape. The manual mode is inefficient, dangerous, and labor-intensive. The automatic mode spends a lot of time on trajectory planning and programming. To overcome the challenges, a novel teleoperation machining mode and a modified master-slave control structure are proposed. Moreover, a hierarchical control

Efficient and high-quality machining process route planning is crucial in the realm of manufacturing. Traditional methods heavily rely on human-computer interaction, which can be inefficient. To enhance the efficiency of machining process route planning, this paper introduces a novel framework based on deep reinforcement learning (DRL), designed to automatically generate machining process routes for