Spraying is a critical surface treatment process in intelligent manufacturing, and coating quality directly affects product performance. Therefore, efficient, accurate, and intelligent coating defect detection is an essential technique to ensure product reliability. The past decade has witnessed rapid progress in coating defect detection techniques. However, most existing studies have focused on specific methods or application scenarios, and there is a lack of systematic reviews that provide a comprehensive overview of this particular research area. To fill this research gap, this paper systematically reviews recent advances in coating defect detection, which covers methods from physical property-based non-destructive testing to deep learning-based approaches. Their fundamental principles, applicability in intelligent manufacturing, and current research progress are examined, and key challenges and potential solutions are discussed. Furthermore, integration of advanced intelligent manufacturing technologies into coating defect detection systems is analyzed to enhance system-level digitalization, automation, and efficiency. Finally, future development trends are explored and analyzed, including collaborative perception, cross-modal fusion, and autonomous decision-making. It is expected that this review will help to advance and accelerate theoretical research and engineering applications in coating defect detection by providing researchers with a comprehensive understanding.

Industry 5.0 advocates human-centric smart manufacturing (HSM), with growing attention to proactive human-machine collaboration (HRC). Meanwhile, the rapid development of Multimodal large language models (MLLMs) and embodied intelligence is driving an unprecedented evolution. This work aims to leverage these opportunities to enhance robots’ learning and cognitive capabilities, enabling seamless and natural interaction. However, current research often overlooks human–robot symbiosis and lacks attention to specialized models and practical applications. This review adheres to a human-centric vision, taking language as the pivot to connect humans with large models. To our best knowledge, this is the first attempt to integrate HRC, MLLMs and embodied intelligence into a holistic view. The review first introduces representative foundation models to provide a comprehensive summary of state-of-the-art methods in the ”Perception-Cognition-Actuation” loop. It then discusses pathways and platforms for efficient spatial skills learning, followed by an analysis of four key questions from the ”Why, How, What, Where” perspectives. Finally, it highlights future challenges and potential research directions. It is hoped that this work can help fill the research gap between HRC and MLLMs, offering a systematic pathway for developing human-centered collaborative systems and promoting further exploration and innovation in this exciting and crucial field. The resources are available at: https://github.com/WuDuidi/MLLM-HRC-Survey.

The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels.

Calibration between robots and cameras is critical in automated robot vision systems. However, conventional manually conducted image-based calibration techniques are often limited by their accuracy sensitivity and poor adaptability to dynamic or unstructured environments. These approaches present challenges for ease of calibration and automatic deployment while being susceptible to rigid assumptions that degrade their performance. To close these limitations, this study proposes a data-efficient vision-driven approach for fast, accurate, and robust hand–eye camera calibration, and it aims to maximise the efficiency of robots in obtaining hand–eye calibration images without compromising accuracy. By analysing the previously captured images, the minimisation of the residual Jacobian matrix is utilised to predict the next optimal pose for robot calibration. A method to adjust the camera poses in dynamic environments is proposed to achieve efficient and robust hand–eye calibration. It requires fewer images, reduces dependence on manual expertise, and ensures repeatability. The proposed method is tested using experiments with actual industrial robots. The results demonstrate that our NBV strategy reduces rotational error by 8.8%, translational error by 26.4%, and the number of sampling frames by 25% compared to artificial sampling. The experimental results show that the average prediction time per frame is 3.26 seconds.

In the context of human-centric smart manufacturing, human-robot collaboration (HRC) systems leverage the strengths of both humans and machines to achieve more flexible and efficient manufacturing. In particular, estimating and monitoring human motion status determines when and how the robots cooperate. However, the presence of occlusion in industrial settings seriously affects the performance of human pose estimation (HPE). Using more sensors can alleviate the occlusion issue, but it may cause additional computational costs and lower workers' comfort. To address this issue, this work proposes a visual-inertial fusion-based method for HPE in HRC, aiming to achieve accurate and robust estimation while minimizing the influence on human motion. A part-specific cross-modal fusion mechanism is designed to integrate spatial information provided by a monocular camera and six Inertial Measurement Units (IMUs). A multi-scale temporal module is developed to model the motion dependence between frames at different granularities. Our approach achieves 34.9 mm Mean Per Joint Positional Error (MPJPE) on the TotalCapture dataset and 53.9 mm on the 3DPW dataset, outperforming state-of-the-art visual-inertial fusion-based methods. Tests on a synthetic-occlusion dataset further validate the occlusion robustness of our network. Quantitative and qualitative experiments on a real assembly case verified the superiority and potential of our approach in HRC. It is expected that this work can be a reference for human motion perception in occluded HRC scenarios.

Digital Twin (DT) of a manufacturing system mainly involving materials and machines has been widely explored in the past decades to facilitate the mass customization of modern products. Recently, the new vision of Industry 5.0 has brought human operators back to the core part of work cells. To this end, designing human-centric DT systems is vital for an ergonomic and symbiotic working environment. However, one major challenge is the construction and utilization of high-fidelity digital human models. In the literature, preset universal human avatar models such as skeletons are mostly employed to represent the human operators, which overlooks the individual differences of physical traits. Besides, the fundamental utilization features such as motion tracking and procedure recognition still do not well address the practical issues such as occlusions and incomplete observations. To deal with the challenge, this paper proposes a systematic design framework to quickly and precisely build and utilize the human-centric DT systems. The mesh-based customized human operator models with rendered appearances are first generated within one minute from a short motion video. Then transformer-based deep learning networks are developed to realize the motion-related operator status synchronization in complex conditions. Extensive experiments on multiple real-world human–robot collaborative work cells show the superior performance of the proposed framework over the state-of-the-art.

In recent years, as a promising way to realize smart manufacturing, digital twin shop-floor has attracted more and more attentions. Frontier researches have preliminarily shown that the interaction, which is the core feature of digital twin, is beneficial for dynamic analysis, real-time production management and remote shop-floor control. However, current research pays scant attention to the seamless control under uncertain conditions, which could lead to outdated or ineffective control because of the interaction delay and uncertainty. To address this problem, this paper firstly proposed a digital twin shop-floor construction framework towards seamless control under uncertain conditions. Moreover, connotations of seamless and resilient control are also introduced. Then, the Lego-style modeling and configuring method of digital twin shop-floor are discussed, to provide the basis for digital twin shop-floor development and seamless control. Predictive interaction mechanism for resilient control is further explained in detail. Finally, a digital twin shop-floor for chemical fiber production is chosen as the case to validate the effectiveness and feasibility of proposed framework and method.

The conventional automation approach has shown bottlenecks in the era of component assembly. What could be automated has been automated in some high tech industrial production, leaving manual work performed by humans. To achieve ergonomic working environments and better productivity, human–robot collabora tion has been adopted for this purpose through combining the strength, accuracy and repeatability of robots with adaptability, high-level cognition, and flexibility. A reliable human–robot collaborative setting should be supported by dynamically updated and precise models. For this purpose, the digital twin can realise the digital representation of physical collaborative settings through simulation modelling and data synchronisation but is limited by communication delay and constraints. This chapter will develop a digital twin-enabled approach to human–robot collaborative assembly. Within this approach, a sensor-driven 3D modelling of the physical devices of interest is developed to realise the physical-to-digital transformation of human–robot workcell, and a Wise-ShopFloor-based platform enabled by sensor data is used to develop a digital twin model of the physical human–robot workcell. Then, function blocks with embedded algorithms are used for assembly planning, decision making and robot control, and a time-ahead execution and planning approach is developed for reliable human–robot collaborative assembly. Finally, the performance of the developed system is demonstrated by a case study of a partial car engine assembly.

At present, the tool remaining useful life prediction technology is important to the effectiveness of machining, because tool life prediction plays the role of safety maintenance, cost optimization and quality assurance. However, this the technology faces many challenges in practical applications. The main problems include that when the spatial distribution of data features is too different, the model is difficult to adapt to multi-scene data and the feature extraction of data time series is not obvious. Therefore, this paper proposes a method for predicting the remaining useful life of milling cutters by using multi-scale spatial data feature visualization and domain separation prediction network (MTF-SE-DSPNs). Firstly, the one-dimensional time series data are globally normalized by this method, and then the processed data are transformed into images by MTF, which enhances the time series features expression ability of data. At the same time, the convolutional neural network based on DenseNet architecture is used and SElayer is added to adjust the feature weight to mine the sensitive information in the signal. To improve the prediction ability of the model, the time decay factor ξT is introduced to optimize the reconstruction loss, so that it can dynamically measure the relative importance of source domain and target domain data and improve the robustness of feature information reconstruction. Finally, the effectiveness of the method is validated by milling experiments under the same and different working conditions. The experimental results are compared with the other models, which proves the significant advantages of the model in various tasks.

Industrial robots (IRs) serve as critical equipment in advanced manufacturing systems. Building high-fidelity digital twin models of IRs is essential for various applications like precision simulation, and intelligent operation and maintenance. Despite technological potentials of digital twins, existing modeling methods for industrial robot digital twins (IRDTs) predominantly focus on isolated domains. This fails to address inherent multi-domain complexities of IRs that arise from their integrated mechanical-electrical-control characteristic. To bridge this gap, first, this study proposes a multi-level multi-domain (MLMD) digital twin modeling framework and method. The framework systematically integrates physical space, digital space, and their bidirectional interactions, while explicitly defining hierarchical structures and cross-domain mechanisms. Subsequently, a four-step method is established, which encompasses component analysis, parameter extraction, MLMD IRDT modeling based on function blocks (FBs), and model validation. Then, implementation details are illustrated through an SD3/500 IR case study, where domain-specific modeling techniques and cross-domain integration mechanisms are systematically analyzed. Finally, effectiveness and feasibility of the proposed method is validated through experiments.