The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training for each problem instance. Monte Carlo tree search is a powerful planning algorithm that strategically explores simulated future possibilities, but it requires a discrete problem representation that is

Soft robotics hardware, with numerous applications ranging from health care to exploration of unstructured environments, suffers from limited life cycles, which lead to waste generation and poor sustainability. Soft robots combine soft or hybrid components via complex assembly and disassembly workflows, which complicate the repair of broken components, hinder upgradability, and ultimately reduce their

Offshore wind turbine structures experience combined wind and wave loading during their lifetime, and the cyclic characteristics of these loads significantly impact the fatigue life of the support structure. Continuous monitoring of stress-related quantities, such as strain time histories at hotspot locations of the structure, can help to estimate the fatigue life. However, sparse measurements from

Internal resonance isa unique phenomenon in nonlinear structures. It can lead to pronounced energy transfer between vibrating modes, causing unexpected stress concentrations in local areas and posing a risk to structural integrity. This paper presents a general model updating procedure for nonlinear structures with such internal resonances using experimentally measured data. The test involves using

Linear friction welding is a solid-state joining technology that bonds materials via friction heating and plastic deformation. This process is being extensively researched for welding metallic sheets with different dimensions; however, it involves difficulties in joining thin cross-sections due to extensive misalignment and unsteady plastic extrusion of softened materials at interfaces. This study

In modern organic light-emitting diode (OLED) manufacturing systems, scheduling is a key decision-making problem to improve productivity. In particular, the scheduling of evaporation and encapsulation stages has been confronted with complicated constraints such as job-splitting property, preventive maintenance, machine eligibility, family setups, and heterogeneous release time of jobs. To efficiently

Robotic machining has been increasingly applied in intelligent manufacturing production lines. Compared with the traditional machine tools, commissioning for robotic machining system (RMS) is particularly important due to the low accuracy of industrial robots (IRs). Traditional site commissioning has large workload and is difficult to handle the multi-source errors. Since digital twin (DT) provides

Combined with virtual sensing techniques, active noise control can generate quiet zones in places without error microphones. Many existing virtual sensing techniques require pretraining of the system with physical microphones placed temporarily at the control location as well as retraining if the primary sound field or the secondary paths change. This process responds slowly and is infeasible in many

Mechanical vibrations in low-frequency resonance and harmonic modes severely affect the closed-loop performance of inertially stabilized platforms (ISPs). This article proposes a new vibration control scheme capable of suppressing low-frequency vibration with large amplitude and harmonic disturbances in application to an ISP. Concretely, we first design an optimized repetitive control (ORC) framework

High-speed imaging is essential for high-frequency vibration measurement techniques that utilize images, such as digital image correlation (DIC). However, it poses challenges, including reduced displacement resolution due to decreased image resolution, increased measurement and calculation costs, and greater data volume. A previous method used compressed sensing to reconstruct time information from

Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural

Structural health monitoring (SHM) is essential for managing infrastructure by continuously monitoring performance. To address the complexity of SHM for large systems, this study introduces a dynamic graph neural network (DynGNN) approach for near-real-time damage identification. The approach represents the infrastructure as a graph and uses a dynamic adjacency matrix based on proper orthogonal decomposition

This paper proposes a hierarchical Bayesian model updating approach to quantify variability of aerodynamic stiffness and damping of an offshore wind turbine (OWT) under different environmental and operational conditions (EOCs) using in-situ vibration data and SCADA (supervisory control and data acquisition) over two months of continuous monitoring. The considered OWT is a Haliade 150, 6 MW GE turbine

This paper presents a novel baseline-free online method for fatigue crack detection and growth modelling of aluminium plates based on the relative second harmonic parameter β′ˆ, which is defined as the ratio of the amplitude of the fundamental frequency A1 over the square of the second harmonic frequency A2, obtained from the response under cyclic fatigue loading. Results reveal that β′ˆ is an effective

This research contributes to the field of reliability engineering and system safety by introducing aninnovative diagnostic method to enhance the reliability and safety of complex technological systems. Steam turbines are specifically referred to. This study focuses on the integration of advanced signal processing techniques and engineering dynamics in addressing critical issues in the monitoring and

Tracking vibration displacement across multiple points on large-scale bridges in the field poses significant challenges due to perspective distortion by camera tilting, interference from adverse environmental factors and low-resolution images by long distance measurement. To address these issues, this study proposed a novel vision-based displacement measurement framework that combined the Fast Spatio-Temporal

The identification of bushings dynamics is crucial for the development of innovative model-based digital tools, such as for example predictive maintenance or virtual sensing. Quite a few nonlinear modelling techniques have been proposed over the years, which can be suitably used to develop advanced models that capture the linear and nonlinear dynamics of a vast range of mechanical components and materials

This paper proposes a framework for mixed uncertainty quantification in the remaining service life assessment of existing deteriorated reinforced concrete (RC) bridges under temperature climate changes. The probability bound analysis (PBA) is deployed for quantifying mixed uncertainty. The epistemic uncertainty in climate change is modelled using a distribution-free p-box in the PBA framework. An extension

This paper presents a new framework for investigating the impact of heterogeneity levels on the fracture properties of hydrogels, offering guidelines for the application of heterogeneous structure design principles. The study reveals that heterogeneous hydrogel structures generated by higher inhomogeneity levels β exhibit increased fracture toughness compared to homogeneous ones, though excessively

Lattice structures are lightweight and are known to exhibit excellent energy absorbing capability when subject to compressive loading. In this paper, a new analytical model for the stiffness, strength, and energy absorption of additively manufactured functionally graded lattice structures is presented, leading to the establishment of a new energy absorption optimisation approach. The influence of cell

DNA has shown great biocompatibility, programmable mechanical properties, and precise structural addressability at the nanometer scale, rendering it a material for constructing versatile nanorobots for biomedical applications. Here, we present the design principle, synthesis, and characterization of a DNA nanorobotic hand, called DNA NanoGripper, that contains a palm and four bendable fingers as inspired

Molecular motors generate force to individually power molecular machines or collectively drive macroscopic actuators. The force output of molecular and macroscale motors appears to be constrained by the same scaling law relating motor force and mass. Here, potential origins of these universal performance characteristics are discussed and the implications examined.

In cells, proteins rapidly self-assemble into sophisticated nanomachines. Bioinspired self-assembly approaches, such as DNA origami, have been used to achieve complex three-dimensional (3D) nanostructures and devices. However, current synthetic systems are limited by low yields in hierarchical assembly and challenges in rapid and efficient reconfiguration between diverse structures. Here, we developed

Increasingly functional microscopic machines are poised to have massive technical influence in areas including targeted drug delivery, precise surgical interventions, and environmental remediation. Such functionalities would increase markedly if collections of these microscopic machines were able to coordinate their function to achieve cooperative emergent behaviors. Implementing such coordination

Triangular mesh representation is extensively utilized in geometric design and reverse engineering. However, in the realm of high quality CNC machining, there is a notable transition from mesh to continuous surface representation for workpieces. This paper presents a novel approach to address this shift, proposing a high-precision and efficient path generation method of ball-end milling specifically

Thermal barrier coatings (TBCs) are crucial for protecting high-temperature components in gas turbines and aeroengines. However, conventional non-destructive testing (NDT) methods, such as infrared thermography, face challenges in precisely detecting and quantifying debonding defects due to limitations in imaging contrast and sensitivity to surface states. In this work, a novel scheme for imaging debonding

When the rubber O-ring follows the primary seal in the axial direction as a secondary seal, there is hysteresis behavior between the restoring force and displacement. However, the dynamic parameters of O-rings are mostly expressed as constants in dynamic analysis at present, which cannot accurately describe the dynamic behavior of sealing systems. In response to the problem of poor track performance

Remaining useful life (RUL) prediction is crucial to supporting intelligent maintenance and health management of safety–critical products. Although advanced data-driven approaches such as deep neural networks are effective in processing high-dimensional non-linear health features, their application to field RUL prediction confronts with two challenges: (a) adaptivity of the lifetime parameter learning

Nonlinear hysteretic isolators are widely applied in the field of vibration isolation due to their excellent energy dissipation capacity. However, the dynamic response prediction of nonlinear hysteretic isolators highly depends on accurate modeling methods for nonlinear hysteresis under varying conditions. This paper proposes a dynamic response prediction model based on quasi-static characteristics

Seismic base isolation faces the challenge of the large displacement demands for the isolators in case of long-period ground motions. Semi-active control has the potential to mitigate the risk against such extreme scenarios and therefore increase the seismic reliability of base-isolated structures. However, the superstructural nonlinearity and the time delay in the control system are always neglected

Effective health monitoring and fault diagnosis technologies are crucial to timely grasping the operation status of the planetary gear train (PGT). In this study, the mechanism for global dynamic transmission error (GDTE)-based crack diagnosis of PGT is revealed from dynamic responses and validated through experiments, and the advantages of this new technique are compared with the commonly used vibration-based

In this paper we present the Physics-Aware Deep-NMF (PAD-NMF) algorithm and we apply it to hit detection in mechanical systems from audio mixtures. The algorithm accurately extracts the acoustic emission made by the physical source to be monitored, to be properly used in engineering analysis, like e.g. the hit detection here considered. This is mainly a source separation problem where sources have

Long-period vibrations are very common in various flexible structures, such as high-rise buildings and floating structures. Inspired by latching-based phase optimization in wave energy converters (WECs), a latching control mechanism is applied to realize long-period vibration control. Based on pendulum tuned mass dampers which are a traditional type of vibration control device, an innovative pendulum