Robotics is increasingly seen as a useful test bed for computational models of the brain functional architecture underlying animal behavior. We provide an overview of past and current work, focusing on probabilistic and dynamical models, including approaches premised on the free energy principle, situating this endeavor in relation to evidence that the brain constitutes a layered control system. We

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator–like controllers, which, in turn, are informed by

Digital twin is regarded as the virtual counterpart of physical entities, which can mirror the physical behavior and performance. Digital twin technology provides strong support for the achievement of cyber-physical system and intelligent manufacturing. Many investigations have been carried out for the digital twin of specific products. However, there are less researches on digital twin in the shop-floor

Acoustic Luneburg lens (ALL) is a spherically/circularly symmetric gradient refractive index lens, whose index varies smoothly from the outer surface to the centre. The variation of the index can be achieved via the changing of filling ratio of unit cells or varying the structural thickness based on specific governing equations. The fundamental principles were used to explain the acoustic wave propagation

In this paper, the robustness of subspace-based identification methods for modal analysis is assessed for damaging systems including local Coulomb-friction mechanisms (or equivalently elasto-plasticity). The underlying objective is to capture damage-induced modal feature changes of structures, mainly frequency drop-off and damping increase, knowing that the presence of components with participating

The wear and rolling contact fatigue (RCF) testing approaches for wheels and rails have been reviewed and evaluated in this study. The study points out the advantages and limitations of the existing approaches. The broad analysis revealed that scaled laboratory-based wear testing is widely applied. However, it is necessary to predetermine the input parameters and observing parameters for scaled wear

In noncontact machining, such as welding and spraying, running efficiency and smoothness have been a bottleneck problem in trajectory optimisation of industrial robots. When the dynamic and mechanical properties of robots are fully utilised, significant impact are often produced. Thus, reducing the process impact of the robot and achieving a balance between the efficiency and smoothness of the operation

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly

Industrial robots have been extensively used in industry, however, geometric errors mainly caused by connecting rod parameter error and non-geometric errors caused by deflection and friction, etc., limit its application in high-accuracy machining. Aiming at addressing these two types of errors, parametric methods for error compensation based on the kinematic model and non-parametric methods of directly

In the era of Industry 4.0, Production Logistic Digital Twins (PLDTs) have garnered remarkable attention from both academic and industrial communities. This is evident from the growing number of research publications on PLDTs in international scientific journals and conferences. However, given the diversity and complexity of production logistics activities, there is a pressing need for systematic literature

Due to issues such as limited variability in monitoring data across different tool wear conditions and interference during the machining process, data-driven monitoring models are susceptible to misclassification. Therefore, this paper proposes a pioneering approach that takes into account the tool wear law and the characteristic distribution of tool wear monitoring data. Specifically, the paper proposes

This study investigates the potential to improve the fault detection capability of sparsity-based blind filtering. It optimizes a finite impulse response filter to maximize the sparsity of the squared envelope spectrum (SES) of vibration signals. However, the method is to be highly prone to fail optimization due to the immense number of non-fault-related second-order cyclostationary interferences.

Existing chatter detection methods were mainly developed for conventional milling processes, and did not consider the influences of environment noises. Thus, they are not suitable for micro milling, in which the widely used small cutting parameters easily lead to the occurrence of non-negligible noises. This article proposes a new variable forgetting factor recursive least-squares (VFF-RLS) algorithm

The vibration data are quite important for structural health monitoring (SHM). This paper proposed a novel method, to adaptively estimate video motions of the structure in subpixel accuracy, without attaching any targets. The proposed method includes three steps. In the first step, to remove outliers and simultaneously preserve feature points, the Gaussian range kernel is used along with the Gaussian

Freeform surface parts, such as blades, exhibit complex structures and excellent aerodynamic performance, making them widely utilized in aerospace propulsion systems. However, monitoring and ensuring surface quality during the milling process of such components is challenging, leading to high scrap rates and unguaranteed processing efficiency. To address these issues, this paper investigated the milling

Condition monitoring signals provide sufficient information about the health of machines and, therefore, are widely used for fault diagnosis, prognosis, and health management. Existing approaches generally extract one or more degradation features from original signals collected in a time interval and predict the remaining useful life of machinery based on a selected or fused feature under a pre-specified

Nowadays, leveraging data augmentation-based methods to address the data-shortage problem in diagnosis field becomes fairly prevailing, while assessing the quality of the generated data receives little attention. Typically, the data quality is evaluated by straightforward employing some existing shallow functions or simple classification models, which have several disadvantages. In this paper, the

A digital twin (DT) is an effective means of achieving physical and information fusion and has great potential for achieving a new paradigm of cutting process (CP) intelligence. This paper traces the relevant studies of digital technology in manufacturing and proposes a bionic digital brain (BDB) as the intelligent core of a digital twin-cutting process (DTCP) framework. The BDB was built with digital

In this paper, an inversion scheme for the profile reconstruction of a fatigue crack in rails from the motion-induced eddy current testing (MIECT) signals is proposed based on the conjugate gradient (CG) optimization method and the fast MIECT forward simulator developed by authors. To calculate the gradient of MIECT signals with respect to the crack parameters, a simplified analytical gradient formula

Abstract not available

Health indicator (HI), which aims to make quantitative measures for machinery operating state at different degradation stages, is very critical in machinery condition monitoring. Some HIs from different aspects have been developed and reported in recent years. However, a preferable HI which is more robust to transient interferences, free of complicated model training and also sensitive to incipient

Machine health monitoring is an important domain to provide timely anomaly detection and diagnostic supports for condition based maintenance. Health indicator (HI) construction is an intuitive and efficient way to conduct continuous machine health monitoring. In this study, a maximization-entropy optimization oriented interpretable HI is proposed to locate informative fault frequencies for machine

Making full use of gyro effect to adjust resonance speed distribution is of great significance for avoiding resonance design of high-speed rotor system. Existing rotor dynamics research often regards gyro effect term as an implicit quantity to be solved in dynamic equation of complex rotors, but does not carry out quantitative evaluation on its gyroscopic effect. Therefore, this paper decomposes kinetic

In this study, broadband frequency-response of a bi-stable piezo-magneto-elastic harvester is extended by analytically characterizing many subharmonic-n solutions. The energy principle-based model considers the continuous analytical mode-shapes of a compound beam, rotational inertia of the tip mass, and nonlinear geometry effect. A modified higher-order Harmonic Balance (HB) approach proposed here

In this work, we introduce a novel method, namely Spectral Optimization-based Modal Identification (SOMI) for estimating the modal parameters of structures. SOMI is developed to estimate highly accurate modal damping ratios and mode shapes using the Frequency Domain Decomposition (FDD) principles. The FDD method (and most of the present modal identification techniques), encounters some problems such

Laser powder bed fusion (L-PBF) is a metal additive manufacturing (AM) process with great potential in producing high performance metal components. Due to lack of stability and repeatability of the building process, its wide application in industry is limited. The process monitoring and control are import to ensure product quality. The size and shape of the melt pool are continuously changing during

Intelligent mechanical fault diagnosis techniques have been extensively developed in recent years. Owing to the advantage of data privacy protection, federated learning has recently received increasing attention; this approach can utilize monitoring data from multiple local clients to train an optimal global diagnosis model. However, low-quality data are often present for some clients, including mislabeled