Search Results (331)

Pilot production is a critical transitional phase in the process of new product development or manufacturing, aiming at ensuring that products are thoroughly validated and optimized before entering full-scale production. During this stage, a key challenge is how to leverage limited resources to build data infrastructure and conduct data analysis to establish and verify quality control. This paper presents the implementation of a cyber–physical system (CPS) for a lithium battery pilot assembly line. A machine learning-based predictive model was employed to establish quality control mechanisms. Process knowledge-guided data analysis was utilized to build a quality prediction model based on the collected battery data. The model-centric concept of ‘virtual quality’ enables early quality judgment during production, which allows for flexible quality control and the determination of optimal process parameters, thereby reducing production costs and minimizing energy consumption during manufacturing. Full article

Amid the accelerated digital transformation and with the growing demand for smart manufacturing, the applications of the Internet of Things (IoT) and total quality management (TQM) have attracted increasing attention. Using R for bibliometric analysis, we explored research trends in IoT and TQM in terms of digital transformation and smart manufacturing. Data were gathered from the Web of Science from 1998 to 2025, with a total of 787 publications from 265 sources involving 2326 authors. A total of 31% of the authors collaborated internationally, indicating global interest in this topic. The publications had 33.65 citations on average, totaling 33,599 citations. Wang L.H. and Tao F. were identified as important authors. Keywords of “Industry 4.0”, “cyber-physical systems”, and “big data” underscore the technological significance of IoT and TQM. Major journals such as the Journal of Manufacturing Systems and IEEE Access had notable academic influence. Co-citation analysis results revealed that IoT and TQM played a significant role in driving digital transformation and enhancing production efficiency, offering references for enterprises in strategic planning for smart manufacturing. Full article

In the context of Industry 4.0, Total Productive Maintenance (TPM) is undergoing a major shift driven by digital technologies such as the IoT, AI, cloud computing, and Cyber–Physical systems. This study explores how these technologies reshape traditional TPM pillars and practices through a two-phase methodology: bibliometric analysis, which reveals global research trends, key contributors, and emerging themes, and a systematic review, which discusses how core TPM practices are being transformed by advanced technologies. It also identifies key challenges of this transition, including data aggregation, a lack of skills, and resistance. However, despite the growing body of research on digital TPM, a major gap persists: the lack of a standardized model applicable across industries. Existing approaches are often fragmented or too context-specific, limiting scalability. Addressing this gap requires a structured approach that aligns technological advancements with TPM’s foundational principles. Taking a cue from these findings, this article formulates a systematic and scalable framework for TPM 4.0 deployment. The framework is based on four pillars: modular technological architecture, phased deployment, workforce integration, and standardized performance indicators. The ultimate goal is to provide a basis for a universal digital TPM standard that enhances the efficiency, resilience, and efficacy of smart maintenance systems. Full article

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article

In recent decades, climate change, increasing demand, and resource scarcity have transformed the agricultural sector into a critical field of research. Farmers have been compelled to adopt innovations and new technologies to enhance production efficiency and crop resilience. This study presents a systematic literature review, supplemented by a bibliometric analysis of relevant documents, focusing on the key applications and combined techniques of artificial intelligence (AI), machine learning (ML), and digital twins (DT) in the development and implementation of cyber-physical systems (CPS) in smart agriculture and establishes whether CPS in agriculture is an attractive research topic. A total of 108 bibliographic records from the Scopus and Google Scholar databases were analyzed to construct the bibliometric study database. The findings reveal that CPS has evolved and emerged as a promising research area, largely due to its versatility and integration potential. The analysis offers researchers and practitioners a comprehensive overview of the existing literature and research trends on the dynamic relationship between CPS and its primary applications in the agricultural industry while encouraging further exploration in this field. Additionally, the main challenges associated with implementing CPS in the context of smart agriculture are discussed, contributing to a deeper understanding of this topic. Full article

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains. Full article

The European Union (EU) is striving to achieve its goal of being climate-neutral by 2050. Aligned with the European Green Deal and in search of means to decarbonize its urban environments, the EU advocates for smart positive energy districts (PEDs). PEDs contribute to the United Nations’ (UN) sustainable development goals (SDGs) of “Sustainable Cities and Communities”, “Affordable and Clean Energy”, and “Climate Action”. PEDs are urban neighborhoods that generate renewable energy to a higher extent than they consume, mainly through the utilization of innovative technologies and renewable energy sources. In accordance with the EU 2050 aim, the PED concept is attracting growing research interest. PEDs can transform existing energy systems and aid in achieving carbon neutrality and sustainable urban development. PED is a novel concept and its implementation is challenging. This study aims to present the emerging technologies enabling the proliferation of PEDs by identifying the main challenges and potential solutions to effective adoption and implementation of PEDs. This paper examines the importance and utilization of cyber-physical systems (CPSs), digital twins (DTs), artificial intelligence (AI), the Internet of Things (IoT), edge computing, and blockchain technologies, which are all fundamental to the creation of PEDs for enhancing energy efficiency, sustainable energy, and user engagement. These systems combine physical infrastructure with digital technologies to create intelligent and autonomous systems to optimize energy production, distribution, and consumption, thus positively contributing to achieving smart and sustainable development. Full article

An important step toward automation and digitization in Industry 4.0 is the automobile sector’s use of smart manufacturing integrated systems (SMISs). Although this change increases productivity and competitiveness, it also creates new hazards for workplace safety. Key issues include ergonomic and cognitive strain from greater human–machine interactions, particularly with collaborative robots (cobots), and cybersecurity threats from the IIoT and cyber–physical systems. This paper looks at these hazards and stresses the value of safety precautions like predictive maintenance, traceability, and real-time monitoring. This case study investigates how the integration of smart manufacturing integrated systems (SMISs) and cyber–physical systems (CPSs) within Industry 4.0 frameworks enhances workplace safety in the automotive sector. Through a comprehensive case study of the final assembly line, this research explores how these technologies contribute to predictive maintenance, real-time monitoring, and human–machine collaboration, leading to significant reductions in ergonomic and cybersecurity risks. Full article

A Digital twin (DT) enables the acquisition and subsequent analysis of large amounts of process data. Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. However, there is very little understanding of the relative merit of these methods. This paper proposes a DT framework in the context of predictive maintenance in smart manufacturing to compare the prediction efficacy of prevalent ML models. Data-driven models were developed using machine learning algorithms to predict surface roughness and power consumption during a CNC turning operation. Three process parameters, namely cutting velocity, feed rate, and depth of cut, and two dependent parameters, surface roughness and power consumption, were selected for model development. Seven ML algorithms were tested for each response parameter: Linear Regression, XGB Regressor, Random Forest Regressor, Average Ensemble, AdaBoost Regressor, SVR, and MLP. The results of the comparative analysis of the ML algorithms showed that the Random Forest Regressor is the best prediction model for surface roughness, with the highest R² (94.2% ± 2.4%), lowest MAE (0.011 ± 0.002), lowest MAPE (15.6% ± 4.0%), and lowest RMSE (0.017 ± 0.003), while the XGB Regressor demonstrated the best performance for power consumption prediction, with the highest R² (98.9% ± 0.5%), lowest MAE (22.513 ± 4.424), lowest MAPE (3.0% ± 0.5%), and lowest RMSE (42.650 ± 8.933). The best-performing machine learning algorithm was subsequently utilized in the data-driven models, helping to achieve an improved surface finish. This enables predictive maintenance, reducing energy usage for more sustainable production. Full article

Robots and 3D scanning systems are essential in modern industrial production, enhancing quality control, reducing costs, and improving production efficiency. Such systems align with Industry 4.0 trends, incorporating the Internet of Things (IoT), Big Data, Cyber–Physical Systems, and Artificial Intelligence to drive innovation. This paper focuses on the physical adaptation of old or out-of-use articulated robot arms for new tasks such as manipulation with a handheld 3D scanner, with the goal of automated quality control. The adaptation was carried out using a methodology that features the application of several techniques such as 3D digitization (photogrammetry), reverse engineering and 3D modeling (SolidWorks), the CAD search engine (3Dfindit), and 3D printing (fused deposition modeling—FDM). Reconstructed 3D models were used to design connecting elements, such as gripper jaws. The final results show that it is possible to create a connecting element utilizing this approach with very little expenditure of resources and time. Full article

During this period, as distributed energy resources (DERs) are crucial for meeting energy needs and renewable technology advances rapidly, photovoltaic (PV)-powered energy islands (EIs) requiring a constant energy supply have emerged. EIs represent a significant milestone in the global energy transformation towards clean and sustainable energy production. They play a vital role in the future energy infrastructure, offering both environmental and economic benefits. In this context, reliance on information and communication technologies for system management raises concerns regarding the cybersecurity vulnerabilities of PV-supported EIs. In other words, since EIs transmit power through power converters—integral cyber-physical components of these systems—they are uniquely susceptible to cyber-attacks. To tackle this vulnerability, a cyber-attack detection scheme using a supervised machine learning (SML) model is proposed. The initial goal is to ensure the transfer and maintenance of energy demands without power loss for critical loads by detecting cyber-attacks to establish a defense mechanism. Two distinct artificial neural network (ANN) structures are implemented to identify cyber threats and support subsequent power demand, resulting in a complementary approach. The findings reveal the model’s effectiveness, demonstrating high accuracy (e.g., a cross-entropy loss of 12.842 × 10⁻⁴ for ANN-I with a 99.98% F1 score and an MSE of 1.0934 × 10⁻⁷ for ANN-II). Therefore, this work aims to open the fundamental way for addressing this issue, particularly concerning hijacking attacks and false data injection (FDI) cyber-attacks on PV-powered EIs. The success of this model and its outcomes confirm the effectiveness of the proposed approach method. Full article

Cyber-physical systems (CPSs) are fundamental components of Industry 4.0 production environments. Their interconnection is crucial for the successful implementation of distributed and autonomous production plans. A particularly relevant challenge is the optimal scheduling of tasks that require the collaboration of multiple CPSs. To ensure the feasibility of optimal schedules, two primary issues must be addressed: (1) The design of global systems emerging from the interconnection of CPSs; (2) The development of a scheduling formalism tailored to interconnected Industry 4.0 settings. Our approach is based on a Category Theory formalization of interconnections as compositions. This framework aims to guarantee that the emergent behaviors align with the intended outcomes. Building upon this foundation, we introduce a formalism that captures the assignment of operations to cyber-physical systems. Full article

Machine learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an interactive causality-enabled self-labeling method has been proposed to advance adaptive ML applications in cyber–physical systems, especially manufacturing, by automatically adapting and personalizing ML models after deployment to counter data distribution shifts. The unique features of the self-labeling method require a novel software system to support dynamism at various levels. This paper proposes the AdaptIoT system, comprising an end-to-end data streaming pipeline, ML service integration, and an automated self-labeling service. The self-labeling service consists of causal knowledge bases and automated full-cycle self-labeling workflows to adapt multiple ML models simultaneously. AdaptIoT employs a containerized microservice architecture to deliver a scalable and portable solution for small and medium-sized manufacturers. A field demonstration of a self-labeling adaptive ML application is conducted with a makerspace and shows reliable performance with comparable accuracy at 98.3%. Full article

The increasing number and complexity of cyber–physical systems in vehicles necessitate a rigorous approach to identifying functional safety and cybersecurity hazards during the concept phase of product development. This study establishes a systematic method for identifying safety and security requirements for E/E components in the automotive sector, utilizing the SysML language within the CAMEO environment. The method’s activities and work products are grounded in the ISO 26262:2018 and ISO/SAE 21434:2021 standards. Comprehensive requirements were defined for the method’s application environment and activities, including generic methods detailing the creation of work products. The method’s metamodel was developed using the MagicGrid framework and validated through an application example. Synergies between the two foundational standards were identified and integrated into the method. The solution generation was systematically described by detailing activities for result generation and the production of standard-compliant work products. To facilitate practical implementation, a method template in SysML was created, incorporating predefined stereotypes, relationships, and tables to streamline the application and enhance consistency. Full article

The objective of this study is to conduct an analysis of the scientific literature on the application of the Internet of Things (IoT) and artificial intelligence (AI) in enhancing supply chain operations. This research applies a dual approach combining bibliometric analysis and topic modeling to explore both quantitative citation trends and qualitative thematic insights. By examining 810 qualified articles, published between 2011 and 2024, this research aims to identify the main topics, key authors, influential sources, and the most-cited articles within the literature. The study addresses critical research questions on the state of IoT and AI integration into supply chains and the role of these technologies in resolving digital supply chain management challenges. The convergence of IoT and AI holds immense potential to redefine supply chain management practices, improving productivity, visibility, and sustainability in interconnected global supply chains. This research not only highlights the continuous evolution of the supply chain field in light of Industry 4.0 technologies—such as machine learning, big data analytics, cloud computing, cyber–physical systems, and 5G networks—but also provides an updated overview of advanced IoT and AI technologies currently applied in supply chain operations, documenting their evolution from rudimentary stages to their current state of advancement. Full article