The Industrial Internet of Things (IIoT) has emerged as a potent catalyst for transformation across many industries as a part of Industry 4.0. This review thoroughly examines IIoT applications, demonstrating how it enhances operational efficiency, informed decision-making, cost optimization, innovation, and workplace safety. While prior research has often concentrated on technical dimensions such as

In image enhancement for low-illumination images, deep learning methods based on the Retinex theory typically decompose the image into illumination and reflectance, followed by iterative optimization or the use of prior custom enhancements. The reflectance map is then approximated as the enhanced image by dividing the radiance by the illumination map. However, this approach does not account for the

In the manufacturing industry, the demand for fault-prediction solutions is increasing to prevent unexpected downtimes and reduce maintenance costs. Although deep-learning methods have demonstrated excellent performance in this domain, the current methods typically overlook the analysis of variable and random processes within mixed-model production, which is a manufacturing strategy that offers flexibility

As rotating machinery often operates under complex and variable harsh conditions, domain generalization-based fault diagnosis has been adopted to tackle the challenge of distribution shifts and unseen data in target domains. However, most existing methods depend on fully labeled data from multiple source domains to learn domain-invariant representations. In practice, collecting comprehensive labeled

User centric smart products prioritize the needs and preferences of users, enhancing their experience and satisfaction. Involving users in the design and assessment of smart products ensures that they meet real-world requirements, leading to more intuitive product design, user interface, and functionalities that truly resonate with users. Further, the capability of generating and evaluating many alternative

In the hot rolling industry, uneven roll wear significantly influences schedule free rolling and product quality, necessitating more precise wear prediction to improve the capabilities of hot rolling production. However, existing methods, laden with limitations, struggle to predict uneven roll wear precisely and transparently. To address these challenges, we present a novel paradigm that combines a

Accurately predicting the future wear of cutting tools with variable geometric parameters remains a significant challenge. Existing methods lack the capability to model long-term temporal dependencies and predict future wear values—a key characteristic of world models. To address this challenge, we introduce the Tool-Multimodal Generative Pre-trained Transformer (Tool-MMGPT), a novel and scalable multimodal

Stamped parts play a crucial role in industrial manufacturing, and it is particularly important to automatically inspect their surface cracks. Since crack is rare and diverse, supervised defect detection methods lack sufficient data and cannot achieve ideal results. Unsupervised anomaly detection algorithms, which do not require crack data, can identify unknown cracks. However, they tend to have high

Particle-reinforced metal matrix composites (PRMMCs) are widely used because of their exceptional material properties. Online control of the laser field to soften and modify the reinforcing particles on the machined surface of the composites is an effective way to improve the machinability and machining quality of PRMMCs. A real-time segmentation method for reinforcing particles in PRMMCs is proposed

Defect diagnosis based on magnetic flux leakage (MFL) signals is an important process for assessing pipeline health, including defect detection and size quantification. However, existing studies suffer from poor consistency of results, because they regard defect detection and size quantification as separate tasks, lacking paradigm harmonization and interaction. In addition, the calibration of experts

Physics-informed neural networks (PINNs) face significant challenges to predict the vortex shedding in the flow past a two-dimensional cylinder, mainly due to complex loss landscapes, spectral bias, and a lack of inductive bias towards periodic functions. To overcome these challenges, a novel gradient-free PINN (GF-PINN) coupled with a U-Net+ + architecture is proposed. For optimizing the complex loss

In the fast-paced manufacturing industry, rapid and efficient product design is essential for meeting customer demands and maintaining a competitive edge. Despite advancements, transforming 2D design concepts into accurate 3D models remains a complex challenge, primarily due to the non-differentiability of traditional rendering processes that hinder gradient-based optimizations. To address this limitation

The interactions between robotics and Building Information Modeling (BIM) are revolutionizing the construction industry by fostering smarter, more adaptive, and efficient workflows. BIM provides robots with geometric and semantic information for precise task execution, while robots contribute real-time as-built data to update and refine BIM models. Despite its significant potential, research on BIM-robotics

Assembly positioning processes play a crucial role in determining the final manufacturing precision of aircraft components. Traditional methods typically treat components as rigid bodies, focusing on adjusting their position and orientation while overlooking the complexities associated with deformable structures. This paper proposes an innovative methodology to optimize the positioning process of aircraft

Inspecting the underside of large-span bridges is a major challenge due to the extensive area and inaccessibility. This study developed a system that integrates advanced equipment with intelligent algorithms, designed to achieve precise identification and rapid localization of defects on the underside of bridges. The key components of the system are summarized as follows: (1) The dynamic visual perception

In practical application scenarios such as air quality, traffic and mechanical processing, sensors are often constrained by spatial capacity, geometric structures, extreme environments and other factors, making it impossible to place them in critical monitoring areas. To address this issue, a novel virtual sensor state perception generalization framework, the Virtual-Real Spatial-Temporal Dual Layer

This paper presents a novel approach to support complex maintenance procedures through a dialogue-driven digital assistant using an ontology-based retrieval augmented generation method. The core of the proposed system relies on the strong formalisation capabilities of the graph-based Web Ontology Language (OWL), combined with various retrieval algorithms and different Large Language Models (LLMs) to

Advances in CAD (Computer Aided Design) and CAM (Computer Aided Engineering) have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for manufacturing, and digitally tracking the entire process from design to procurement in the form of product

In the rapidly evolving field of soft robotics, the development of new materials, structural designs, and conceptual frameworks has led to the rise of soft robot technology, which is now moving towards a highly versatile modular architecture with potential uses across various industries. However, one of the main hurdles faced in this domain is the shape-morphing challenge, as existing visualization

With the continuous increase in the number of EVs, intelligent charging has become a critical challenge for power supply infrastructure. Charging robots, equipped with batteries and charging guns, are capable of performing autonomous charging and have emerged as a focal point for research for leading robotics companies. However, during the autonomous charging process, several challenges remain, particularly

Industrial gas drying systems with twin silica gel packed beds are widely used to remove moisture from gas streams, alternating between drying and regeneration phases to maintain continuous operation. This paper presents an integrated solution for real-time estimation of water content within the packed beds of such a system used for drying process air. First, two mechanistic models were developed and

In rolling bearing application scenarios, the challenges of acquiring faulty data have led to research focusing on unsupervised fault detection and diagnosis methods trained solely on healthy data. In this study, we built a deep digital twin of a healthy rolling bearing state by combining multi-similarity metrics and a model backpropagation mechanism to fully mine fault information and achieve advancements

3D Reconstruction Using Images has made strides in small-scale, uncomplicated scenes but struggles with complex, large-scale architectural forms. Targeting early-stage architectural design, we introduce ArchiDiff, a platform for 3D architectural form generation and editing from images to point clouds. First, we curated a dataset specifically tailored for architectural design, ArchiCloudNet. Second

This paper explores the domain of adaptive Human-Computer Interaction (HCI) within the emerging context of Industry 5.0, which marks the transition from Industry 4.0 by emphasizing human-centric approaches and collaboration between humans and intelligent systems. It focuses on enhancing user experience while maintaining high security standards. The complexity of intelligent industrial environments

Deflection and lateral displacement are critical factors in bridge structural health monitoring. Vision-based displacement monitoring techniques have advantages, such as full-field coverage, high precision, real-time feedback, and automation. However, existing methods still face two key problems that limit their field application: a) an inherent trade-off between measurement range and accuracy, and

With the ongoing advancements in autonomous navigation technology, agricultural robots are increasingly being deployed across various sectors of agriculture. Among the critical components of this technology, dynamic obstacle avoidance in complex agricultural environments serves as the foundation for enhancing the autonomy and safety of these robots. The Dynamic Window Approach (DWA) is a widely recognized

Despite the extensive use of robots in numerous fields, condition-sensitive robotic machining errors represent a significant obstacle to their high-precision implementation. Prediction-based compensatory control represents a crucial approach to enhancing robot accuracy. The extant machining error prediction methods are beset with shortcomings, including inadequate feature extraction, limited generalizability

In process industry, plant operators often rely on their experience to choose suitable process settings that meet the productivity and quality goals. When these goals are not met, multiple changes to the settings might be necessary, which is time-consuming because each adjustment requires waiting for the new steady-state condition. A digital twin that quickly provides key performance indicators in

The growing availability of metal forming data has driven a new era of data-centric approaches in digital manufacturing. This wealth of data enables the development of digitally enhanced metal forming processes and associated technologies. In this work, using the hot stamping data obtained from a cloud-based manufacturing database, the digital characteristics (DC), defined as the visualisation of a

Surface defect detection is a research hotspot in the field of computer vision. Due to the complex characteristics of metal surfaces and the multitude of industrial defects, it remains a challenging task. In order to meet the requirement of accurate identification of surface defects on copper strips and plates in industrial quality control, we propose a computer vision-based dual-branch features fusion

In high-speed milling, chatter detection plays an important role in ensuring surface quality and safe machining. Traditionally, chatter detection is performed by manually setting the feature threshold, which is unreliable. In this paper, an intelligent chatter detection method is proposed based on deep learning. The proposed method is featured by automatic chatter detection based on multi-channel features

Cyber-physical systems (CPSs) is a general concept that encompasses a wide variety of systems. Depending on their nature, application, and accessibility needs and restrictions, CPSs can differ a lot from each other. This paper proposes a classification of CPSs based on their accessibility needs and restrictions and, more importantly, presents an approach to create a decentralised and worldwide common

Fostered by the recent development in artificial intelligence technologies, digitalization in industries is proceeding towards intelligent automation of various physical work processes with autonomous robotic applications, in dynamic and non-deterministic environments, and in collaboration with human workers. The article presents an explorative case study on designing a cognitive situation-adaptive

Faults occurring during machining processes can severely impact productivity and product quality. Deep learning models have been actively used to develop fault diagnosis approaches. However, it is challenging for industries to adopt the approaches due to their inability to adapt to varying machining conditions. To address the issue, a novel diagnostic approach is designed based on a hybrid convolutional

In recent years, there has been a remarkable shift from automated plants to intelligent production in the industrial context, accelerated by technologies such as artificial intelligence (AI). The ultimate goal is an autonomous plant that is capable of self-regulation and self-optimization. In electronics production, the first approaches have been proposed for deriving and adjusting machine parameters

The degradation of industrial systems is a natural and often unavoidable process. Hidden Markov Models (HMMs) are used for state-based bearing degradation analysis. A challenge arises because bearings can deteriorate in multiple ways, depending on crack locations. To address this, a Multi-Branch Hidden Markov Model (MB-HMM) was developed to handle multiple deteriorations. However, MB-HMM primarily

This study presents an immersive augmented virtuality (AV)-based human-machine interface (HMI) designed to enhance telepresence and operator performance in telerobotic systems. Traditional telerobotic systems often face limitations such as 2D representations of 3D environments, restricted fields of view, and reduced depth perception, all of which hinder operator effectiveness. Although extended reality

To address the limitations of relying on expensive 3D scanners for obtaining foot data in footwear customisation, this paper introduces a novel framework, Measure2Shape, which estimates 3D foot shapes using anthropometric measurement data. To achieve this, we established a large-scale 3D foot dataset with measurement data and created statistical shape models (SSMs) to represent the range of foot variations

As autonomous vehicles are poised to enter public roadways, a major concern is their interaction with pedestrians. It requires attention and ability for pedestrians to interact correctly and for autonomous vehicles to detect pedestrians hence avoiding collisions. We propose a complete pipeline to collect, process and elaborate video data to quantitatively assess the possible occurrence of conflicts

The integration of Model-Based Systems Engineering (MBSE) and Multidisciplinary Design Analysis and Optimization (MDAO) presents a powerful opportunity to enhance early-stage system design, particularly for complex space systems. However, the lack of efficient integration between these methods results in limitations such as unclear boundary between domain models, reduced automation, and challenges

In the era of Industry 4.0, fault tolerance is essential for maintaining the robustness and resilience of industrial systems facing unforeseen or undesirable disturbances. Current methodologies for fault tolerance stages namely, detection, diagnosis, and recovery, do not correspond with the accelerated technological evolution pace over the past two decades. Driven by the advent of digital technologies

Knowledge distillation (KD) is a powerful model compression technique that aims to transfer knowledge from heavy teacher networks to compact student networks via distillation. However, effectively transferring semantic knowledge in industrial settings poses significant challenges. On one hand, the appearance of defects (e.g., size and shape) may vary considerably due to the influence of the industrial

Construction contracts contain critical risk-related information that requires in-depth examination, yet tight schedules for bidding limit the possibility of comprehensive review of extensive documents manually. This research aims to develop models for automating the review of construction contracts to extract information on risk and responsibility that will provide inputs for risk management plans

Building-integrated photovoltaics (BIPV) incorporated with battery energy storage (BES) and building energy flexibility (BEF) system is nowadays increasingly prevalent. During the operation and maintenance (O&M) of BIPV, BES, and BEF, various knowledge is contained and generated. This highlights information interaction among systems and the demand for incorporating diverse domain knowledge. However

Pyramid-structured abrasive belts have been widely used in the field of precision machining of complex surfaces over recent years. However, continuous wear directly affects their machining performance and quality. The lack of effective engineering monitoring methods limits the further application of such abrasive belts. To address this issue, this study presents an acoustic signal monitoring method

Effective waste management has become the key challenge in developing smart cities with the increase in population. Traditional waste management systems are often inefficient, which leads to unnecessary trips, high operational costs, difficulties in tracking waste, and the inefficient use of resources. The proposed work aims to integrate real-time predictive analysis-based waste collection and disposal

Human Digital Twins (HDTs) are an emerging concept with the potential to create human-centric systems for Industry 5.0. The concept has rapidly spread to new application domains, most notably Healthcare, leading to diverging conceptual interpretations. This Systematic Literature Review analyses the conceptual understanding of HDTs across all application domains to clarify the conceptual foundation

Surface characterisation is essential in metrology for precise measurement and analysis of surface features, ensuring product quality and compliance with industry standards. Form removal is the primary step in surface characterisation, isolating features of interest by eliminating the primary shape from measurements. Traditional least-squares methods, as specified in ISO standards, are effective but

Explainable Artificial Intelligence (XAI) methods are valuable tools for promoting understanding, trust, and efficient use of Artificial Intelligence (AI) systems in business organizations. However, the question of how organizations should select suitable XAI methods for a given task and business context remains a challenge, particularly when the number of methods available in the literature continues

Assembly instructions are detailed directives used to guide the assembly of products across various manufacturing sectors. As production processes evolve to become more flexible, the significance of assembly instructions in meeting rigorous efficiency and quality standards becomes increasingly pronounced. Nevertheless, the development of assembly instructions often remains unstructured and predominantly

Remote Photoplethysmography (rPPG) is a cost-effective and non-contact technology that enables real-time monitoring of physiological status by extracting vital information such as heart rate (HR). This capability enables the assessment of fatigue and stress, helping to prevent accidents by identifying risky conditions early. Continuous monitoring with rPPG reduces operational risks, contributing to

In the research of health status detection technology for complex equipment such as liquid rocket engines, the extreme working environment hinders the widespread conduct of fault experimental simulations, leading to data scarcity and imbalance. Consequently, the performance of intelligent models deteriorates rapidly with direct training. To address this issue, this paper proposes a meta-task feature

In modern industry, timely health assessments of aero-engines are crucial for ensuring their proper functionality and the safety of aviation operations. However, during the collection of operating data for aero-engines, influential fault features may exhibit hysteresis or even overwhelmed due to transmission delays in some sensors. Furthermore, these features in the data at interval points are difficult

In the computer-aided industry, particularly within the domain of agricultural automation, fruit pose detection is critical for optimizing efficiency across various applications such as robotic harvesting, aerial crop surveillance, precision pruning, and automated sorting. These technologies enhance productivity and precision, addressing challenges posed by an aging labor force and the increasing demand

The agri-food supply chain (AFSC) operations are becoming challenging due to globalization, constantly shifting consumer demands, and intensive disruptions leading to inefficient production and distribution of safe and high-quality food. Technological advancements are the most promising ways to ensure firms’ survival and supply chains. To enhance the resilience of AFSCs, the present study aims to identify

Iron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing

Placing fabric poses a challenge to robots since fabric with high dimensional configuration space can deform during manipulation. Existing methods for placing fabric mostly rely on static operations, which are inefficient and require a large workspace. Therefore, this study applies dynamic manipulation (manipulating uncontrollable parts of the fabric by swinging) to fabric placement, proposing a novel