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Traditional manufacturing workshop processes are challenging to change, and passive monitoring methods, such as quality traceability and completion rate monitoring, pose challenges for discrete workshops in achieving real-time and accurate production control. Therefore, combined with the digital twin system, a multi-level production status monitoring model is established to monitor discrete manufacturing workshops actively. This article focuses on discrete manufacturing workshops' modeling and self-evolution production processes. It also proposes an active monitoring architecture for production status in discrete workshops using digital twin technology. A full-information model is constructed for a discrete manufacturing workshop. Then, a multi-node interaction logic is established for the discrete manufacturing workshop. Finally, based on the fuzzy C-means clustering algorithm, production anomalies are monitored, and real-time monitoring of disturbances caused by various production factors, including material status, equipment status, and work-in-progress status, is performed to assess their impact on production. Based on deep Bayesian network learning algorithms, we can monitor production performance and analyze workshop capacity, equipment utilization, bottleneck rhythm, etc. This allows us to actively monitor production performance through the self-evolution of digital twin models. This article aims to proactively predict changes in workshop production status and provide support for further plan completion rate improvement, capacity prediction, and capacity optimization. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has surfaced as a pivotal component in the evolving field of intelligent manufacturing, offering an array of benefits compared to conventional production techniques. Nevertheless, the industry grapples with issues relating to manufacturing instability and inconsistent repeatability, making it challenging to meet desired microstructure and performance standards. The optimization of processing variables within specific equipment and parameter sets often necessitates expensive trial-and-error experiments, given the diversity and intricacy of AM process parameters. To mitigate these challenges, the digital twin (DT) technical concept has been implemented to bolster AM by offering real-time projection and mirroring of physical attributes for both the fabricated products and the AM machinery, thereby facilitating real-time feedback control to alleviate AM-induced defects and achieve optimal performance of the manufactured parts. DT techniques streamline process monitoring, performance prediction, anomaly detection, process parameter optimization, and production cost forecasting, thereby enhancing the entire AM process. Within the framework of Industry 4.0, DTs in AM have attracted considerable attention and experienced significant progress. Auxiliary techniques such as the Internet of Things (IoT), big data analysis, cloud manufacturing, and machine learning (ML) have substantially driven the expansion of DTs in AM. This review's contribution lies in the comprehensive analysis of how the digital twin (DT) technical concept has been introduced to enhance AM. This review examines existing literature on DTs in AM from six perspectives: background information, structural components, applications, directions for improvement, principal issues encountered, and potential research directions. It identifies current advancements, discusses applications across different domains, suggests areas for improvement, and outlines potential research directions. This review also identifies current advancements, discusses applications across different domains, suggests areas for improvement, and outlines potential research directions. These insights significantly contribute to the understanding and further development of DTs in AM within the context of Industry 4.0, offering a fresh perspective that aligns with the evolution of the intelligent manufacturing industry. [ABSTRACT FROM AUTHOR]

Digital twins (DT) have increasingly garnered attention in today's manufacturing industry for their potential to enhance productivity, efficiency, and decision-making processes. This study presents a human-centric digital twin framework (HCDT) specifically designed for a three-axis vertical milling machine (VMC3), following the ISO 23247 standard. Our system seeks to offer a comprehensive virtual depiction of a tangible milling machine, facilitating immediate monitoring, analysis, and enhancement of machining operations. This study targets specifically the prediction of power consumption in three-axis vertical machining using advanced machine learning. Power consumption during modified Kakino toolpath cutting was examined through a series of trials that involved adjusting spindle speed, depth of cut, and feed rate. The analysis was conducted utilizing physical sensors, mathematical modeling, and machine learning. The utilization of machine learning methods, specifically random forest, exhibited encouraging outcomes, as evidenced by a mean absolute error (MAE) of 17.30. In addition, virtual simulations were performed to forecast power usage. The proposed applied digital twin (ADT) incorporates human intelligence and artificial intelligence to effectively integrate physical and virtual environments, offering a unique approach that adheres to the ISO 23247 framework. The cross-system entity of the digital twin showcases that the calculated power has large variations with the experimental power. However, the proposed ADT approach has resulted in higher similarity to the calculated, experimental, and predicted power consumption respectively in the proposed digital twin scenario. Also, the addition of explainability to the result has developed the trustworthiness in the digital twin environment. [ABSTRACT FROM AUTHOR]

The problem of achieving a good maintenance plan is well-known in the modern industry. One of the most promising approaches is predictive maintenance, which schedules interventions based on predictions made by collecting and analyzing data from the process. However, to the best of the authors' knowledge, this approach is still not widespread and known enough, and particularly, the real-case scenarios of its application appear not exhaustive. To contribute to fill this gap, this work proposes a digital twin (DT), which performs a predictive maintenance approach for a conveyor belt within a real-case scenario with the overall goal of predicting faults during normal belt operations. Specifically, the core of the implemented DT is a model that analyzes the data collected by various sensors distributed along the conveyor belt. In turn, this model exploits a machine learning-based algorithm that predicts the insurgence of faults. The tests of the developed solution, conducted within a real scenario, demonstrated good precision and accuracy in identifying the fault status and also in a time deemed acceptable for the involved stakeholders. [ABSTRACT FROM AUTHOR]

As the manufacturing industry is moving towards the fourth industrial revolution, there is an increasing need for smart maintenance systems that could provide manufacturers with a competitive advantage by predicting failures. Despite various efforts by researchers, there are still challenges for these systems to work reliably in the industry such as lack of adaptability, resilience, reaction to disturbances, and future-proofing. Bio-inspired frameworks like artificial immune systems provide an alternative approach to satisfying these challenges. But existing immune-based frameworks focus only on adaptive immunity characteristics and ignore innate immunity which is important for quick detection and faster response. There is a need for a holistic view of the immune system in developing an adaptive & resilient maintenance framework. This paper presents a holistic view of the human immune system with a focus on the intelligence & response mechanism of both innate & adaptive immunity. Inspired by this holistic view and considering the emerging computer technologies — Internet of Things, Edge & Cloud Computing, Multi-Agent System, Ontology, Big Data, Digital Twin, Machine Learning, and Augmented Reality — we present a smart maintenance framework. The proposed framework is used for tool condition monitoring to demonstrate its implementation. [ABSTRACT FROM AUTHOR]

Skiving has potential for gear machining, but its cutting force fluctuates greatly, its cutting temperature is high during processing, and it has the characteristics of thermomechanical coupling. It is difficult for traditional methods to dynamically predict the thermomechanical coupling during the skiving process, whose efficiency and stability cannot be guaranteed. Aiming to solve these problems, a digital twin (DT)-based dynamic method is proposed to predict thermomechanical coupling in the skiving process. Considering the time-varying characteristics and coupling of the cutting force and temperature, a multi-physical modeling method dual-driven by mechanism and data is proposed to establish a thermomechanical coupling DT (TMDT) model of the skiving process. The dynamic consistency of the skiving process between the digital and physical spaces is realized. Principal component analysis (PCA) and an extreme learning machine (ELM) are used to reduce the order of the TMDT, the reduced-order model is trained using the skiving big data, and a relationship mapping model of the cutting parameters and the cutting force and temperature is established to realize the dynamic prediction of the cutting force and temperature. The effectiveness of the proposed method is verified through gear skiving experiments. This research has important theoretical guiding significance to realize efficient and stable skiving processing. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI)-driven predictive maintenance (PM) into the management of medical 3D printing machines promises a revolutionary advancement in operational efficiency and reliability. Traditional maintenance approaches often fail to optimize machine uptime and reduce costs effectively. AI and Machine Learning (ML) techniques, however, enable a proactive maintenance strategy by predicting potential failures and facilitating timely interventions. This ensures continuous high-quality production, which is critical for patient safety and effective medical care. AI-driven PM employs various methods, including supervised and unsupervised learning, neural networks, and natural language processing, to analyze vast datasets and perform intelligent diagnostics. The incorporation of advanced technologies such as digital twins, generative AI, collaborative robots, blockchain, and the Industrial Internet of Things (IIoT) further enhances fault detection, transparency, and efficiency in maintenance processes. The benefits of this approach include minimized downtime, cost savings, improved safety, and increased equipment reliability. As AI technology advances, its role in PM will become more sophisticated, establishing new benchmarks for the operation and management of 3D printing machines. This proactive maintenance strategy not only improves performance but also provides a competitive advantage in the healthcare sector. [ABSTRACT FROM AUTHOR]