Search Results (23)

This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R² up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article

The rapid emergence of Industry 4.0 technologies has transformed manufacturing, requiring a workforce skilled in automation, data-driven decision-making, and process optimisation. While traditional education includes structured formats such as lectures and tutorials, it may not always equip graduates with the hands-on expertise demanded by modern industrial challenges. This study presents a Mixed Reality (MR)-based educational framework that promotes interactive experiences to enhance students’ engagement with and understanding of Industry 4.0 concepts, aiming to bridge the skills gap through immersive Virtual Learning Factories (VLFs). The framework was developed using a mixed-methods approach, combining qualitative feedback with quantitative benchmarking. A proof-of-concept MR application was developed and tested at the (Anonymised), simulating Industry 4.0 scenarios in an engineering education context to validate the framework. The findings indicate that MR-based learning improved students’ engagement with the academic content, leading to better knowledge retention and deeper conceptual understanding. The students also demonstrated enhanced problem-solving, process optimisation, and adaptability compared to traditional methods. The immersive nature of MR provided an interactive, context-rich environment that fostered active learning. This research highlights MR’s potential as a transformative educational tool, aligning academic training with industry needs. Future research is recommended to evaluate the framework’s scalability and long-term effectiveness. Full article

In recent years, the semiconductor industry has embraced advanced artificial intelligence (AI) techniques to facilitate intelligent manufacturing throughout their organizations, with particular emphasis on virtual metrology (VM) systems. Nonetheless, the practical application of data-driven virtual metrology for product quality inspection encounters notable hurdles, such as annotating inspections in highly dynamic industrial environments. This leads to complexities and significant expenses in data acquisition and VM model training. To address the challenges, we delved into transfer learning (TL). TL offers a valuable avenue for knowledge sharing and scaling AI models across various processes and factories. At the same time, research on transfer learning in VM systems remains limited. We propose a novel parameter transfer learning (PTL) architecture for VM systems and examine its application in industrial process automation. We implemented cross-factory and cross-recipe transfer learning to enhance VM performance and offer practical advice on adapting TL to meet individual needs and use cases. By leveraging extensive data from Seagate wafer factories, known for their large-scale and high-dimensional nature, we achieved significant PTL performance improvements across multiple performance metrics, with the true positive rate (TPR) increasing by 29% and false positive rate (FPR) decreasing by 43% in the cross-factory study. In contrast, in the cross-recipe study, TPR increased by 27.3% and FPR decreased by 6.5%. With our proposed PTL architecture and its performance achievements, insufficient data from the new manufacturing sites, new production lines and new products are addressed with shorter VM model training time and smaller computational power with strong final quality prediction confidence. Full article

Generative AI (GenAI) is revolutionizing digital twins (DTs) for fault diagnosis and predictive maintenance in Industry 4.0 and 5.0 by enabling real-time simulation, data augmentation, and improved anomaly detection. DTs, virtual replicas of physical systems, already use generative models to simulate various failure scenarios and rare events, improving system resilience and failure prediction accuracy. They create synthetic datasets that improve training quality while addressing data scarcity and data imbalance. The aim of this paper was to present the current state of the art and perspectives for using AI-based generative DTs for fault diagnosis for predictive maintenance in Industry 4.0/5.0. With GenAI, DTs enable proactive maintenance and minimize downtime, and their latest implementations combine multimodal sensor data to generate more realistic and actionable insights into system performance. This provides realistic operational profiles, identifying potential failure scenarios that traditional methods may miss. New perspectives in this area include the incorporation of Explainable AI (XAI) to increase transparency in decision-making and improve reliability in key industries such as manufacturing, energy, and healthcare. As Industry 5.0 emphasizes a human-centric approach, AI-based generative DT can seamlessly integrate with human operators to support collaboration and decision-making. The implementation of edge computing increases the scalability and real-time capabilities of DTs in smart factories and industrial Internet of Things (IoT) systems. Future advances may include federated learning to ensure data privacy while enabling data exchange between enterprises for fault diagnostics, and the evolution of GenAI alongside industrial systems, ensuring their long-term validity. However, challenges remain in managing computational complexity, ensuring data security, and addressing ethical issues during implementation. Full article

The widespread adoption of the Industry 5.0 paradigm puts people and their applications at the center of attention and, with the increasing automation and robotization of work, the need for workers to acquire new, more advanced skills increases. The development of artificial intelligence (AI) means that expectations for workers are further raised. This leads to the need for multiple career changes from life and throughout life. Belonging to a previous generation of workers makes this retraining even more difficult. The authors propose the use of machine learning (ML), virtual reality (VR) and brain–computer interface (BCI) to assess the conditions of work–life balance for employees. They use machine learning for prediction, identifying users based on their subjective experience of work–life balance. This tool supports intelligent systems in optimizing comfort and quality of work. The potential effects could lead to the development of commercial industrial systems that could prevent work–life imbalance in smart factories for Industry 5.0, bringing direct economic benefits and, as a preventive medicine system, indirectly improving access to healthcare for those most in need, while improving quality of life. The novelty is the use of a hybrid solution combining traditional tests with automated tests using VR and BCI. This is a significant contribution to the health-promoting technologies of Industry 5.0. Full article

The evolving landscape of industrial manufacturing is increasingly embracing automation within smart factories. However, the critical role of human operators, particularly in manual assembly and disassembly tasks, remains undiminished. This paper explores the complexities arising from mass customization and remanufacturing, which significantly enhance the intricacy of these manual tasks. Human involvement is essential in these tasks due to their complexity, necessitating a structured learning process to enhance efficiency and mitigate the learning–forgetting cycle. This study focuses on the utilization of virtual reality (VR) as an innovative training tool to address these challenges. By conducting a systematic literature review (SLR) on the impact of VR on training operators for assembly and disassembly tasks, this paper evaluates the current level of VR application, the used technologies, the operator performance, and the VR benefits and limitations. The analysis reveals a limited but promising application of VR in training, highlighting its potential to improve learning outcomes, productivity, and safety while reducing costs. However, the research also identifies gaps in the practical application of VR for training purposes suggesting a future research agenda to explore its full potential. Full article

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability. Full article

In the context of Industry 4.0 and smart manufacturing, production factories are increasingly focusing on process optimization, high product customization, quality improvement, cost reduction, and energy saving by implementing a new type of digital solutions that are mainly driven by Internet of Things (IoT), artificial intelligence, big data, and cloud computing. By the adoption of the cyber–physical systems (CPSs) concept, today’s factories are gaining in synergy between the physical and the cyber worlds. As a fast-spreading concept, a digital twin is considered today as a robust solution for decision-making support and optimization. Alongside these benefits, sectors are still working to adopt this technology because of the complexity of modeling manufacturing operations as digital twins. In addition, attempting to use a digital twin for fully automatic decision-making adds yet another layer of complexity. This paper presents our framework for the implementation of a full-duplex (data and decisions) specific-purpose digital twin system for autonomous process control, with plastic injection molding as a practical use-case. Our approach is based on a combination of supervised learning and deep reinforcement learning models that allows for an automated updating of the virtual representation of the system, in addition to an intelligent decision-making process for operational metrics optimization. The suggested method allows for improvements in the product quality while lowering costs. The outcomes demonstrate how the suggested structure can produce high-quality output with the least amount of human involvement. This study shows how the digital twin technology can improve the productivity and effectiveness of production processes and advances the use of the technology in the industrial sector. Full article

VR has shown positive growth in the world in recent years, which is mainly due to projects such as learning, games, entertainment and experiential activities. VR has changed the way of life of users, providing users with more interesting interactions and immersive experiences. This study aims to investigate students’ practical capabilities and learning effectiveness under the instruction strategy of integrating virtual reality into simulation games into the Vehicle Body Electrical System Comprehensive Maintenance and Repair Services Practice curriculum for students of the Dept. of Auto Mechanics in a skills-based senior high school. Two student classes of the Dept. of Auto Mechanics major in Electrical Engineering featuring practical subjects in one skills-based senior high school in central Taiwan were chosen as the participants for this study. By way of pretest–post-test research design and heterogeneous grouping, an 8-week instruction experiment was conducted in which ZPD (zone of proximal development) instruction strategies were used in the experimental group (with 43 persons), while traditional didactic instruction strategies were used in the control group (with 36 persons). ZPD instructional strategies analyze and collect quantitative and qualitative data to investigate the instructional effectiveness and feasibility in developing ZPD as the research material in the practical curriculum for the study area of the Power Machinery in Vehicle Body Electrical System Comprehensive Maintenance and Repair Services practice. According to the research objective, the results are concluded as follows. (1) Students achieved the best learning effectiveness when adopting ZPD (zone of proximal development) strategies in which virtual reality was integrated into the vehicle charging and starting system to investigate students’ automotive diagnostic troubleshooting and fault-clearing capabilities. (2) Students attained the highest acceptance in learning phenomenon when adopting ZPD (zone of proximal development) strategies in which virtual reality was integrated into students’ familiar practice factory environment and the tools and equipment operation process. (3) Students had a higher acceptance of learning effectiveness when using virtual reality simulation games in the disassembly and functional detection of vehicle charging and starting systems. (4) There is a positive effect when integrating virtual reality simulation games into ZPD instruction strategies in the knowledge, skills and attitude on students’ overall inquiry practical capabilities and their learning effectiveness. Full article

Recently, a novel approach in the field of Industry 4.0 factory operations was proposed for a new generation of automated guided vehicles (AGVs) that are connected to a virtualized programmable logic controller (PLC) via a 5G multi-access edge-computing (MEC) platform to enable remote control. However, this approach faces a critical challenge as the 5G network may encounter communication disruptions that can lead to AGV deviations and, with this, potential safety risks and workplace issues. To mitigate this problem, several works have proposed the use of fixed-horizon forecasting techniques based on deep-learning models that can anticipate AGV trajectory deviations and take corrective maneuvers accordingly. However, these methods have limited prediction flexibility for the AGV operator and are not robust against network instability. To address this limitation, this study proposes a novel approach based on multi-horizon forecasting techniques to predict the deviation of remotely controlled AGVs. As its primary contribution, the work presents two new versions of the state-of-the-art transformer architecture that are well-suited to the multi-horizon prediction problem. We conduct a comprehensive comparison between the proposed models and traditional deep-learning models, such as the long short-term memory (LSTM) neural network, to evaluate the performance and capabilities of the proposed models in relation to traditional deep-learning architectures. The results indicate that (i) the transformer-based models outperform LSTM in both multi-horizon and fixed-horizon scenarios, (ii) the prediction accuracy at a specific time-step of the best multi-horizon forecasting model is very close to that obtained by the best fixed-horizon forecasting model at the same step, (iii) models that use a time-sequence structure in their inputs tend to perform better in multi-horizon scenarios compared to their fixed horizon counterparts and other multi-horizon models that do not consider a time topology in their inputs, and (iv) our experiments showed that the proposed models can perform inference within the required time constraints for real-time decision making. Full article

As an alternative for university students to continue their professional training during the COVID-19 pandemic, higher education institutions implemented virtual learning modalities. In this context, it was proposed to determine the social representations assumed by university students who are pursuing their studies as future educators. It is presumed that representations related to didactic practices are composed of content (knowledge, skills, and attitudes) and organization (central core and representational system). This is an ethnographic study, with an available nonprobabilistic sample of 227 students from the primary education major at Universidad Técnica del Norte. Verbal association techniques and documentary research were used for information collection. To analyze the data, the IRaMuTeQ software (R interface for texts and questionnaire multidimensional analysis) was used. Two types of analyses were conducted: hierarchical classification and factorial correspondence. In conclusion, a virtuality with difficulties and a careful and responsible face-to-face modality are expressed as meanings associated to the representations, both of which require qualitative changes. Regarding the organization, didactic practice complementarity is assumed to be integrated in a hybrid learning modality. Full article

Industry 4.0 is evolving through technological advancements, leveraging information technology to enhance industry with digitalisation and intelligent activities. Whereas Industry 5.0 is the Age of Augmentation, striving to concentrate on human-centricity, sustainability, and resilience of the intelligent factories and synergetic industry. The crucial enhancer for the improvements accomplished by digital transformation is the notion of ‘digital triplet D3’, which is an augmentation of the digital twin with artificial intelligence, human ingenuity, and experience. digital triplet D3 encompasses intelligent activities based on human awareness and the convergence among cyberspace, physical space, and humans, in which Implementing useful reference hierarchy is a crucial part of instigating Industry 5.0 into a reality. This paper depicts a digital triplet which discloses the potency of retrofitting a conventional drilling machine. This hierarchy included the perceptive level for complex decision-making by deploying machine learning based on human ingenuity and creativity, the concatenated level for controlling the physical system’s behaviour predictions and emulation, the observing level is the iterative observation of the actual behaviour of the physical system using real-time data, and the duplicating level visualises and emulates virtual features through physical tasks. The accomplishment demonstrated the viability of the hierarchy in imitating the real-time functionality of the physical system in cyberspace, an immaculate performance of this paradigm. The digital triplet’s complexity was diminished through the interaction among facile digital twins, intelligent activities, and human awareness. The performance parameters of the digital triplet D3 paradigm for retrofitting were eventually confirmed through appraising, anomaly analysis, and real-time monitoring. Full article

Due to the fourth industrial revolution, manufacturing companies are looking to implement digital twins in their factories to be more competitive. However, the implementation of digital twins in manufacturing systems is a complex task. Factories need a framework that can guide them in the development of digital twins. Hence, this article proposes a small-scale digital twin implementation framework for manufacturing systems. To build this framework, the authors gathered several concepts from the literature and designed a digital twin subsystem model using a model-based systems engineering (MBSE) approach and the systems engineering “Vee” model. The systems modelling defines the digital twin components, functionalities, and structure. The authors distribute most of these concepts throughout the framework configuration and some concepts next to this general configuration. This configuration presents three spaces: physical, virtual, and information. The physical space presents a physical layer and a perception layer. The information space has a single layer called middleware. Finally, the virtual space presents two layers: application and model. In addition to these layers, this framework includes other concepts such as digital thread, data, ontology, and enabling technologies. This framework could help researchers and practitioners to learn more about digital twins and apply it to different domains. Full article

Production scheduling is the key to manufacturing process decision support, which directly affects the efficiency and competitiveness of enterprises. The production process of discrete workshops is complex and changeable, and it is usually difficult to make adjustments quickly and accurately in response to disturbance events. In this paper, a workshop production scheduling method based on digital twin is proposed and applied to the manufacturing workshop of an aerospace factory. Combined with the advantages of real-time virtual real interaction fusion of digital twin technology, the dynamic scheduling problem under fault disturbance factors is studied. A high-fidelity digital twin workshop is established to realize the mapping and interaction between the real production and the virtual factory. Based on the vibration data of machine tool spindle, a fault prediction method of learning vector quantization neural network is proposed. The dynamic scheduling strategy of workshop production based on digital twin is constructed and compared with the scheduling results without digital twin under fault disturbance. The results show that the scheduling method based on digital twin can effectively deal with disturbances and improve workshop productivity. This study can be used for the application of digital twin and production scheduling in practical factories. Full article

Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging “hands-on” training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time. Full article