Once a disassembly line is constructed, it typically remains unchanged for an extended period. Consequently, the disassembly of end-of-life (EOL) electronic and electrical appliances for subsequent orders must be planned based on the existing production line configuration after completing the previous order of EOL electronic and electrical appliances. To address this challenge, this study proposed

Water management is becoming an increasingly critical challenge for manufacturing industries due to growing environmental concerns, stricter regulatory requirements, and rising pressure from clients demanding more sustainable practices. Efficient and transparent use of water resources is no longer optional but a strategic necessity across industrial sectors. In this paper a new Lean performance indicator

Benefiting from the digitization of mechanical equipment, the digital twin of smart bearing can better realize the whole life cycle intelligent operation and maintenance of mechanical equipment, where the twin data are normally utilized to realize the state mapping with the identification or prediction model. Whereas, this process is mostly single interaction, and the dynamic update of the twin model

The Digital Twin (DT) is a reciprocally connected and synchronized representation of a physical asset so-called Physical Twin. DT is a central component of a Digital Twin System (DT System), that interconnects the DT and the Physical Twin, through different components, and executes the necessary functions such as exchanging, processing or storing data. The engineering of the DT System is a long and

Multi-sensor fusion has emerged as a solution for accuracy and cost-effective pavement distress detection. However, optimizing the deployment to utilize sensors’ diverse capabilities and enhance their effectiveness has not been thoroughly addressed. Therefore, this study proposed a novel framework for onboard camera-LiDAR deployment optimization. First, mathematical models were formulated to calculate

Embodied intelligence has emerged as a transformative paradigm in artificial intelligence, representing the convergence of multimodal perception, cognitive reasoning, and physical interaction with the environment. In the context of smart manufacturing, it is increasingly recognized as a key enabler for future intelligent systems capable of adapting to dynamic, unstructured, and human-centric production

Three-phase induction motors are the primary actuators for converting electrical energy into mechanical energy in the productive sector, constituting key assets due to their widespread use and critical function. Reducing maintenance costs and implementing predictive techniques incentivize the development of systems to identify intrinsic defects. The increasing demand for customization in manufacturing

In a world facing pressing environmental challenges like climate change and resource scarcity, Artificial Intelligence (AI) is widely regarded as a powerful tool to enhance and support sustainability goals via Circular Economy Capability (CEC). The organizational capacity to leverage this technology, Artificial Intelligence Capability (AIC), is conceptualized through the lens of the Resource-Based

In smart manufacturing, accurate sensor fault diagnosis is essential for operational integrity. However, the direct application of Large Language Models (LLMs) to this task yields unstructured analyses and inefficient resource use. To address these challenges, we propose a novel multi-agent framework that instills a structured, modular, and adaptive reasoning process. The framework features a Reasoning

Substation datasets, like those using the IEC61850 standard, hold sensitive information about power flows, equipment statuses, and network configurations. This data could expose vulnerabilities to knowledge-based cyberattacks, making utility providers hesitant to share it publicly for research. While encryption enhances security, it often diminishes the dataset’s utility for research purposes. To address

Accurate bus mass estimation is crucial for safety and efficiency. However, existing approaches often exhibit limited accuracy when applied to Internet of Vehicles (IoV) data, primarily due to the low sampling rates in practical scenarios. To address this challenge, a framework is proposed for bus mass estimation using IoV data. It focuses on data imputation and information fusion. A Triple Dependency

Manually designing robotic task sequences is labor intensive and inefficient, especially in power inspection tasks that involve academic background knowledge and complex operation rules. To overcome this limitation, this paper presents a large language model-based multi-agent task and motion planning framework, LLM-MTMP, to enable autonomous human–robot interaction and task execution of robot in power

As the structural carrier of mineral resources, underground mine is a typical artificial large layered underground infrastructure. The safety of mining systems remains a critical concern for nations worldwide. Based on the environmental characteristics of underground mines, the accompanying safety issues are evident. Conventional personnel evacuation drills for mine disasters often fail to create effective

Embodied Intelligence (EI) integrates perception, cognition, and action within manufacturing systems, enabling on-device learning and human-machine collaboration. For surface defect inspection, this requires real-time reasoning over subtle 3D geometries and continuous self-improvement using high-quality training data. However, current point cloud generation methods fall short in synthesizing 3D defects

Deep reinforcement learning (DRL) plays a crucial role in complex sequential decision-making tasks. However, existing data-driven DRL methods primarily rely on an empirical risk minimization (ERM) strategy to fit optimal value function models. This approach often neglects the environment’s dynamical system properties, which in turn leads to an inadequate consideration of the structural risk minimization

The perception-control-execution layered architecture, commonly used in current embodied intelligent robot control systems, suffers from inherent latency caused by its serial processing mechanism, which limits a robot's ability to respond to sudden disturbances, such as falls and collisions. To overcome this bottleneck, this study proposes a biomimetic emergency response control architecture for embodied

In the process of multi-cavity hot runner injection molding, the issue of mold filling imbalance caused by uneven temperature distribution significantly affects the quality of precision products such as optical lenses. Traditional methods primarily rely on mold thermal structure design and lack dynamic optimization strategies aimed at product quality. This paper proposes an embodied intelligent online

Modern information and communication technologies have propelled transformative modernization of Industrial Control Systems (ICSs) while exacerbating cybersecurity risks. Federated Learning (FL) offers a privacy-preserving framework for collaborative development of intrusion detection models among distributed participants. However, its effectiveness is significantly limited by inherent model divergence

Internet of Things (IoT) health systems have severe issues distinguishing malicious from legitimate traffic and ensuring secure and efficient data transmission for real-time patient care. Existing solutions have high complexity, low dynamic attack adaptability, and low encryption strength. For the purpose of solving these problems, this study suggests a security-enhanced intelligent edge computing

Supplier selection plays a pivotal role in the construction industry, particularly for companies that seek to harmonize sustainability, innovation, and quality in their operations. This paper introduces a novel multi-criteria decision-making (MCDM) framework that combines the Indifference Threshold-Based Attribute Ratio Analysis (ITARA) and the Mixed Aggregation by Comprehensive Normalization Technique

In modern energy and process industries, the growing demand for continuous, safe, and reliable operation poses increasing challenges to the structural integrity of critical equipment. Erosion, caused by complex gas–solid interactions, is a typical degradation mechanism that affects the service life and operational stability of components such as elbows in syngas pipelines. However, existing monitoring

With the advancement of Artificial Intelligence (AI)-based methods and the establishment of diverse databases, significant research has been conducted on the application of AI in railway track monitoring, inspection and maintenance. Although several review studies exist in this field, each has been confined to a limited scope, focusing on specific data types, data collection methods, or AI-based techniques

Electric vehicle production has recently gained popularity due to increasing emissions. The research on electric vehicle production concerning technical, economic, and environmental aspects is very less compared to the traditional vehicle. This research studies a mixed-type electric vehicle production system that produces spare parts and finally assembles all spare parts for the vehicle. The spare

Ensuring the secure operation of process control systems (PCSs) is critical in the face of increasing cyberattacks. Diagnosing the causes of safety protection failures and predicting potential failure paths during cyberattacks can help maintain the system stability and security. However, cyberattacks often target not only assets but also control tasks or disrupt business processes, rendering existing

The safety of helicopter operations is paramount, yet early signs of potential failures often go undetected, highlighting the need for robust signal alert systems during flights. Detecting anomalies in helicopter engine behavior through vibration analysis is critically important due to the long-sequence nature and complexity of the data, which present significant challenges for real-time assessment

Embodied intelligence (EI) is an emerging frontier in robotics that tightly integrates perception, action, and cognition. By continuously interacting with their environments, EI robots can self-evolve and adapt to uncertainties in flexible assembly tasks, thereby enhancing adaptability and execution efficiency. This paper proposes a behavioral planning and parameter meta learning approach for EI robots

The integration of big data technology with the Internet of Things (IoT) in finance presents an opportunity to enhance credit evaluation and early warning systems for credit risks. Traditional methods face challenges in processing multi-source heterogeneous financial data, leading to inaccuracies in credit risk assessments. This paper proposes an advanced approach using Local and Global Depth Normalization

Sustainability has emerged as a critical concern for manufacturing organisations due to increasing resource scarcity, alongside many other environmental and social concerns. Sustainability assessment models (SAM) play a vital role in evaluating and improving the environmental, economic, and social impacts of manufacturing organisations. To develop a robust sustainability assessment model, it is important

Smart grids (SGs) can greatly improve the efficiency, reliability, and sustainability of traditional grids. In an industrial SG, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. To reduce the burden on SGs, most existing privacy-preserving schemes use aggregated signatures to ensure the integrity of the message and improve communication

To address the challenges of low detection precision for lump coal on underground coal mine conveyor belts, this study proposes an intelligent detection method based on multimodal data fusion. The method is named YOLO DKH (YOLO Dynamic Snake Attention-KANA2-High-level Screening Feature Pyramid Network). This approach specifically targets the insufficient robustness of single-modal data under dust interference

Integrating Agent-Based Modeling (ABM) with the Industrial Internet of Things (IIoT) is reshaping how industries manage complexity, automate decision-making, and improve operational intelligence. This study presents a data-centric ABM–IIoT framework that strengthens the efficiency and responsiveness of industrial systems, particularly in manufacturing and planning domains. The framework supports dynamic

This manuscript presents a literature review with a bibliometric analysis on the Order Batching Problem (OBP). The research analyzed Literature Reviews (30) and Picking Optimization Methods (138). Most approaches focus on hypothetical warehouses with static (offline) orders and are configured as the classical OBP. These warehouses feature rectangular layouts (single-block and parallel aisles) with

The proliferation of cloud computing and web-based services has led to a significant increase in the number and complexity of online web services. As a result, discovering appropriate services that meet user requirements has become a challenging task. Traditional web services discovery techniques often lack the efficiency and adaptability needed to handle user expectations in a dynamic environment

To mitigate the adverse impact of human–machine conflict on driving safety, this paper proposes an indirect shared steering control framework that leverages drivers’ visual and haptic compliance as multimodal embodied cues for adaptive authority allocation. A novel dual-modal compliance index is defined by fusing visual and haptic signals, and a real-time estimation system is implemented alongside

Building digital twins (BDTs) can enhance reliability by integrating real-time data with virtual model, yet most studies still treat virtual model development, fault diagnosis, and in-situ calibration as isolated stages, resulting in fragmented workflows, low automation, and limited interpretability. To address these issues, this study introduces a novel LLM-based AI agent method integrating virtual

Ensuring the cross-sectional shape integrity of medical catheters is a necessity for their safe and effective clinical functionality. Although visual inspection technologies have advanced rapidly, automated inspection of catheter tubes remains challenging due to the complex, deformable structures resulting from extrusion processes and the inherent properties of the materials. In this study, we introduce

Fatigue assessment based on human motion plays a critical role in human-centric intelligent manufacturing, intelligent monitoring, and ergonomics. This growing demand underscores the need for low-cost, high-precision pose estimation techniques with broad application adaptability. To meet these requirements, we propose LightPose, a lightweight human pose estimation framework guided by bone segment principles

This review systematically investigates the corridor allocation problem (CAP) as an important branch of the facility layout problem under the smart manufacturing context. Its aim is to summarise the modeling and variants, classify the solution methods, and identify future trends. We analyse 87 papers published between 2012 and 2024. By systematically categorising and organising these papers, CAP can

Leakage is the main form of failure and safety hazard for a subsea manifold. Timely acquisition of leakage location information is the guarantee for safe subsea oil and gas transportation. The threshold detection and localization method is an important means of identifying the position of subsea leakages and is also one of the few applicable solutions. However, the fixed threshold leads to large errors

Amidst growing demands for rehabilitation, robot-assisted therapy has rapidly evolved as a crucial treatment modality. Despite its potential to enhance outcomes and efficiency, increased adverse events due to human errors remains a significant challenge. To address this issue, we present a novel hybrid Human Error Assessment and Reduction Technique (HEART) that integrates the SHELL model, extended

Industrial surface defect detection is constrained by the scarcity of defective samples and by the insufficient capacity of current segmentation methods to precisely delineate defect boundaries. To address these challenges, we propose F2P-Net, a few-sample, highly precise industrial surface defect segmentation framework composed of three core modules. ViCNet (ViT and CNN collaborative encoder network)

The rapid advancement of e-commerce has driven unprecedented expansion in urban logistics networks, where their sustainability is constrained by multifaceted factors including strict time-bound service requirements, employee’s satisfaction, traffic congestion, and carbon emission regulations. Among these critical elements, employee’s satisfaction reflected by the workload balance not only influences

The increasing complexity of the aerospace industry has highlighted the need to anticipate issues from the entire lifecycle of aircrafts. Identified too late, issues originating from the manufacturing or the maintenance phases can have considerable consequences on the overall development costs, time and quality of aircraft development. Concurrent Engineering (CE) is an approach that aims to improve

The agri-food industry faces complex challenges that impact operational efficiency, profitability, and the ability to meet evolving consumer expectations for transparency and sustainability. Industry 4.0 technologies, particularly the Internet of Things and data analytics, offer substantial potential to enhance performance and support sustainability goals across the supply chain. This study investigates

The Window to the Brain (WttB) is a novel cranial implant designed to enhance therapeutic procedures involving brain tissue. Previous computational models studying the effectiveness of the WttB exhibited some discrepancies with experimental results and inconsistencies in certain parameter values. To overcome these drawbacks, the following steps are followed. We first perform a domain reduction where

The adoption of artificial intelligence (AI) to ensure sustainable cybersecurity practices is a major challenge in the era of Industry 4.0. AI techniques can classify and detect the huge number of cyber-attacks affecting modern industrial systems due to their adaptability and predictability, offering speed of classification, discovery of hidden patterns, and increased accuracy. However, the current

Accurate segmentation of insulated pipelines is essential for automated inspection and structural analysis in complex industrial environments. While UAV-based visual inspection is increasingly adopted, current systems often struggle with cluttered scenes and rely heavily on manual interpretation. This paper proposes Region-Aware Guidance Training (RAGT) for pipeline segmentation, a novel and modular

From the perspective of systems theory, a production process can be conceptualized as an organized set of operations that primarily involves managing the flow of materials, goods, energy, and information. Optimal management of industrial flows is a complex decision-making problem that has been addressed for decades, from modeling and optimization theory to today’s artificial intelligence (AI) techniques

Anomaly detection in production processes is essential for ensuring reliability and efficiency in the industrial sector. In this way, system optimization requires advanced monitoring strategies such as predictive maintenance and intelligent fault detection. Traditional diagnostic methods rely on retrospective data analysis and deterministic cause-effect models, while machine learning approaches enable

Cognition, the set of mental processes that enable humans to perceive, reason, learn and decide, plays an essential role in effective collaboration between humans and Cyber–Physical Systems (CPSs). To achieve seamless cognitive interoperability between humans and CPSs, it is necessary to integrate a Cognitive Digital Twin (CDT) and a Human Digital Twin (HDT) to provide digital representations of both

Digital Twin is a cutting-edge technology designed to address disruptions in manufacturing operations by supporting humans in complex maintenance decisions via advanced data analytics and real-time synchronization. However, as the complexity of decisions increases, enhanced capabilities are required, such as reasoning and context awareness, leading to the Cognitive Digital Twin (CDT) concept. In this

Medical device manufacturers must be able to manage risks at several stages of the product development process and throughout the overall lifecycle. Through the implementation of PLM in healthcare, medical device manufacturers can efficiently oversee the complete lifecycle of their medical products. However, traditional PLM approaches do not adequately address integration with risk management. This

The human digital twin (HDT) is a detailed and personalized digital representation of an individual, encompassing the physical, cognitive, psychological, and social characteristics. HDT, an extension of the traditional digital twin concept from the industrial engineering sector, finds applications in diverse human-centric sectors such as smart manufacturing, medical healthcare, personal fitness, and

High-voltage circuit breakers (HVCBs) present challenges in cross-domain fault diagnosis under zero-shot scenarios due to their complex mechanisms, diverse failure modes, and scarce fault samples. To address this problem, this paper proposes a cross-domain zero-shot diagnosis method for HVCBs driven by multidomain spatial projection (MSP) and dual embedded structure (DES), named MSP-DES. The proposed

Under the industrial internet environment, achieving effective manufacturing process scheduling requires collaboration across the workshop level, production line level, industrial chain, and value chain. This collaboration enables the integration of manufacturing process information, aligning scheduling more closely with practical operations. However, the existing scheduling approaches mainly focus

Debris flows are a significant hazard to human life, infrastructure, and the environment, particularly in mountainous regions where steep terrain and extreme weather intensify their impact. Mitigating these events requires robust decision-making frameworks that can effectively process uncertainty and complex data. This study introduces the score and accuracy functions for T-spherical fuzzy Soft numbers

Industrial system fault information is a fusion of fault-related data, where objects represent core fault events and factors quantify their dynamic states. Factor variations directly reflect object characteristics, driving the System Fault Evolution Process (SFEP)—a complex progression of system functionality from normal operation to failure, shaped by temporal changes in object states and factor values