Search Results (20)

The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private Network (VPN) remote control for assembly and disassembly robotic cell (A/DRC) equipped with a six-Degree of Freedom (6-DOF) ABB 120 industrial robotic manipulator (IRM) is presented in this paper. A three-dimensional (3D) virtual model is developed using Siemens NX Mechatronics Concept Designer (MCD), while the Programmable Logic Controller (PLC) is programmed in the Siemens Totally Integrated Automation (TIA) Portal. A Hardware-in-the-Loop (HIL) simulation strategy is primarily used. This concept is implemented and executed as part of a VC approach, where the designed PLC programs are integrated and tested against the physical controller. Closed loop control and RM inverse kinematics model are validated and tested in PLC, following HIL strategy by integrating Industry 4.0/5.0 concepts. A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. Additionally, it provides communication interfaces via PROFINET IO to SCADA and Human Machine Interface (HMI), and through Open Platform Communication—Unified Architecture (OPC-UA) for Siemens NX-MCD with DT virtual model. Virtual A/DRC simulations are performed using the Synchronized Timed Petri Nets (STPN) model for control strategy validation based on task planning integration and synchronization with other IoT devices. The objective is to obtain a clear and understandable representation layout of the A/DRC and to validate the DT model by comparing process dynamics and robot motion kinematics between physical and virtual replicas. Thus, following the results of the current research work, integrating digital technologies in manufacturing, like VC, IoT, and Cloud, is useful for validating and optimizing manufacturing processes, error detection, and reducing the risks before the actual physical system is built or deployed. Full article

Cloud manufacturing represents a pioneering service paradigm that provides flexible, personalized manufacturing services to customers via the Internet. Service composition plays a crucial role in cloud manufacturing, which focuses on integrating dispersed manufacturing services in the cloud platform into a complete composite service to form an efficient and collaborative manufacturing solution that fulfills the customer’s requirements, having the highest service quality. This research presents the multi-strategy improved artificial rabbit optimization (MIARO) technique, designed to overcome the limitations with the original method, which often risks converging to local optima and have poor solution quality when dealing with optimization problems. MIARO helps the algorithm escape local optimality with Lévy flights, extends local search with the golden sine mechanism, and expands variability with Archimedean spiral mutations. MIARO is experimented on 23 benchmark functions, 3 engineering design problems, and QoS-aware cloud service composition (QoS-CSC) issues at various sizes, and the experimental findings indicate that MIARO delivers outstanding performance and offers a viable solution to the QoS-CSC problem. Full article

A digital twin can be understood as a representation of a real asset, in other words, a virtual replica of a physical object, process or even a system. Virtual models can integrate with all the latest technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI). Digital twins have applications in a wide range of sectors, from manufacturing and engineering to healthcare. They have been used in managing healthcare facilities, streamlining care processes, personalizing treatments, and enhancing patient recovery. By analysing data from sensors and other sources, healthcare professionals can develop virtual models of patients, organs, and human systems, experimenting with various strategies to identify the most effective approach. This approach can lead to more targeted and efficient therapies while reducing the risk of collateral effects. Digital twin technology can also be used to generate a virtual replica of a hospital to review operational strategies, capabilities, personnel, and care models to identify areas for improvement, predict future challenges, and optimize organizational strategies. The potential impact of this tool on our society and its well-being is quite significant. This article explores how digital twins are being used in healthcare. This article also introduces some discussions on the impact of this use and future research and technology development projections for the use of digital twins in the healthcare sector. Full article

Cloud manufacturing allows multiple manufacturers to contribute their manufacturing facilities and assets for monitoring, operating, and controlling common processes of manufacturing and services controlled through cloud computing. The modern framework is driven by the seamless integration of technologies evolved under Industry 4.0. The entire digitalized manufacturing systems operate through the Internet, and hence, cybersecurity threats have become a problem area for manufacturing companies. The impacts can be very serious because cyber-attacks can penetrate operations carried out in the physical infrastructure, causing explosions, crashes, collisions, and other incidents. This research is a thematic literature review of the deterrence to such attacks by protecting IoT devices by employing provenance blockchain and artificial intelligence. The literature review was conducted on four themes: cloud manufacturing design, cybersecurity risks to the IoT, provenance blockchains for IoT security, and artificial intelligence for IoT security. These four themes of the literature review were critically analyzed to visualize a framework in which provenance blockchain and artificial intelligence can be integrated to offer a more effective solution for protecting IoT devices used in cloud manufacturing from cybersecurity threats. The findings of this study can provide an informative framework. Full article

Considering the frequency of ethylene oxide (EtO) residues found in food, the health effects of EtO have become a concern. Between 2022 and 2023, 489 products were inspected using the purposive sampling method in Taiwan, and nine unqualified products were found to have been imported; subsequently, border control measures were enhanced. To ensure the safety of all imported foods, the current study used the K-means clustering method for identifying EtO residues in food. Data on finished products and raw materials with EtO residues from international public opinion bulletins were collected for analysis. After matching them with the Taiwan Food Cloud, 90 high-risk food items with EtO residues and 1388 manufacturers were screened. The Taiwan Food and Drug Administration set up border controls and grouped the manufacturers using K-means clustering in the unsupervised learning algorithm. For this study, 37 manufacturers with priority inspections and 52 high-risk finished products and raw materials with residual EtO were selected for inspection. While EtO was not detected, the study concluded the following: 1. Using international food safety alerts to strengthen border control can effectively ensure domestic food safety; 2. K-means clustering can validate the risk-based purposive sampling results to ensure food safety and reduce costs. Full article

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era. Full article

Airborne pollutants pose a significant threat in the occupational workplace resulting in adverse health effects. Within the Industry 4.0 environment, new systems and technologies have been investigated for risk management and as health and safety smart tools. The use of predictive algorithms via artificial intelligence (AI) and machine learning (ML) tools, real-time data exchange via the Internet of Things (IoT), cloud computing, and digital twin (DT) simulation provide innovative solutions for accident prevention and risk mitigation. Additionally, the use of smart sensors, wearable devices and virtual (VR) and augmented reality (AR) platforms can support the training of employees in safety practices and signal the alarming concentrations of airborne hazards, providing support in designing safety strategies and hazard control options. Current reviews outline the drawbacks and challenges of these technologies, including the elevated stress levels of employees, cyber-security, data handling, and privacy concerns, while highlighting limitations. Future research should focus on the ethics, policies, and regulatory aspects of these technologies. This perspective puts together the advances and challenges of Industry 4.0 innovations in terms of occupational safety and exposure assessment, aiding in understanding the full potential of these technologies and supporting their application in industrial manufacturing environments. Full article

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts. Full article

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented. Full article

The extant literature does not provide consolidated knowledge on the use of Industry 4.0 in supply chains of emerging markets. This systematic literature review investigated the benefits, use, challenges, and mitigation measures related to Industry 4.0 technologies in supply chain management within thirteen “major emerging markets”. Industry 4.0 integrates technologies such as the Internet of Things (IoT), big data analytics, and cloud computing, and it offers tangible benefits for manufacturing and supply chains. However, its adoption faces significant obstacles, particularly in emerging economies. This study used the PSALSAR framework and PRISMA methodology to systematically review 87 peer-reviewed research articles on Industry 4.0 in the supply chain context of thirteen major emerging economies. Findings revealed that while IoT, big data, and artificial intelligence are frequently applied, other technologies such as cloud computing and robotics are underutilized. Key challenges identified include data integration, cyber-security, high upfront investment, weak policy, and business risks. Mitigation strategies proposed include the development of supportive policies, management backing, training, and improved data security. Tangible benefits such as sustainably using resources, reducing power use, enabling collaboration among supply chain partners, incorporating asset traceability, and minimizing meat contamination were evident. This research provides useful insights into the current status of Industry 4.0 adoption in emerging markets, helping stakeholders to navigate towards a more digitized, efficient future. Full article

To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers’ health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers’ health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches. Full article

Facing global market competition and supply chain risks, many production companies are leaning towards distributed manufacturing because of their ability to utilize a network of manufacturing resources located around the world. Deriving from information and communication technologies and artificial intelligence, the digital twin shop-floor (DTS) has received great attention from academia and industry. DTS is a virtual shop-floor that is almost identical to the physical shop-floor. Therefore, multiple physical shop-floors located in different places can easily be interconnected to realize a DT that is a distributed digital twin shop-floor (D2TS). However, some challenges still hinder effective and efficient resource allocation among D2TSs. In order to attempt to address the issues, firstly, this paper proposes an information architecture for D2TSs based on cloud–fog computing; secondly, a novel mechanism of D2TS resource allocation (D2TSRA) is designed. The proposed mechanism both makes full use of a digital twin to support dynamic allocation of geographic resources and avoids the centralized solutions of the digital twin which lead to a heavy burden on the network bandwidth; thirdly, the optimization problem in D2TSRA is solved by a BP neural network algorithm and an improved genetic algorithm; fourthly, a case study for distributed collaborative manufacturing of aero-engine casing is employed to validate the effectiveness and efficiency of the proposed method of resource allocation for D2TS; finally, the paper is summarized and the relevant research directions are prospected. Full article

Cloud manufacturing systems (CMSs) are networked, distributed and loosely coupled, so they face great uncertainty and risk. This paper combines the complex network model with multi-agent simulation in a novel approach to the robustness analysis of CMSs. Different evaluation metrics are chosen for the two models, and three different robustness attack strategies are proposed. To verify the effectiveness of the proposed method, a case study is then conducted on a cloud manufacturing project of a new energy vehicle. The results show that both the structural and process-based robustness of the system are lowest under the betweenness-based failure mode, indicating that resource nodes with large betweenness are most important to the robustness of the project. Therefore, the cloud manufacturing platform should focus on monitoring and managing these resources so that they can provide stable services. Under the individual server failure mode, system robustness varies greatly depending on the failure behavior of the service provider: Among the five service providers (S1–S5) given in the experimental group, the failure of Server 1 leads to a sharp decline in robustness, while the failure of Server 2 has little impact. This indicates that the CMS can protect its robustness by identifying key servers and strengthening its supervision of them to prevent them from exiting the platform. Full article

With the rapid development of the sharing economy, more and more platform operators apply the sharing concept in manufacturing, which increases the efficiency of assets utilization. Considering the apparel industry, clothing enterprises or manufacturers may share their excess orders between each other via a manufacturing cloud platform. Under the traditional production mode, manufacturers focus on processing their individual orders. There may be a coexistence of insufficient and surplus production capabilities. Some manufacturers cannot meet their customer demands due to limited capabilities and some orders have to be rejected, while some other manufacturers may have excess capacities with insufficient demands. It results in loss of revenue, and it is not conducive to maintaining a good customer relationship. In this paper, we consider a shared system with multiple manufacturers that produce homogeneous products, and the manufacturers in the shared system can share customer orders with each other. Once any manufacturer cannot fulfill all of its orders, the unsatisfied ones will be shared with other manufacturers that have surplus capacities with the aim of improving the balance of resource utilization and risk resistance of all manufacturers on the platform. The entropy maximization theory is mainly adopted to facilitate the formulation of the objective function. We apply a Taylor expansion to reformulate the objective function and construct a mixed-integer quadratic programming (MIQP) model. We employ off-the-shelf solvers to solve small-scale problems, and also propose a two-stage constructive heuristic algorithm to solve large-scale problems. Numerical experiments are conducted to demonstrate the efficiency of the algorithm. Full article

In this work, a Detect and Avoid system is presented for the autonomous navigation of Unmanned Aerial Vehicles (UAVs) in Urban Air Mobility (UAM) applications. The current implementation is designed for the operation of multirotor UAVs in UAM corridors. During the operations, unauthorized flying objects may penetrate the corridor airspace posing a risk to the aircraft. In this article, the feasibility of using a solid-state LiDAR (Light Detecting and Ranging) sensor for detecting and positioning these objects was evaluated. For that purpose, a commercial model was simulated using the specifications of the manufacturer along with empirical measurements to determine the scanning pattern of the device. With the point clouds generated by the sensor, the system detects the presence of intruders and estimates their motion to finally compute avoidance trajectories using a Second Order Cone Program (SOCP) in real time. The method was tested in different scenarios, offering robust results. Execution times were of the order of 50 milliseconds, allowing the implementation in real time on modern onboard computers. Full article