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This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solving a challenging particle transport problem. DeepONet also exhibits generalizability and computational efficiency as an efficient surrogate tool for DT component. However, the application of DeepONet reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world DT implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, this study marks an important step towards harnessing the power of DeepONet surrogate modeling for real-time inference capability within the context of DT enabling technology for nuclear systems. [ABSTRACT FROM AUTHOR]

Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, where a constantly changing environment is expected and network configuration must adapt accordingly. Modeling tools are required to predict the impact on performance (packet and delay loss) when new traffic demands arrives and when changes in routing paths are applied in the network. Mathematical analysis and network simulators are techniques for modeling networks but both have limitations, as the former provides low accuracy and the latter requires high execution times. To overcome these limitations, machine learning (ML) algorithms, and more specifically, graph neural networks (GNNs), are proposed for network modeling due to their ability to capture complex relationships from graph-like data while predicting network properties with high accuracy and low computational requirements. However, one of the main issues when using GNNs is their lack of generalization capability to larger networks, i.e., when trained in small networks (in number of nodes, paths length, links capacity), the accuracy of predictions on larger networks is poor. This paper addresses the GNN generalization problem by the use of fundamental networking concepts. Our solution is built from a baseline GNN model called RouteNet (developed by Barcelona Neural Networking Center-Universitat Politècnica de Catalunya (BNN-UPC)) that predicts the average delay in network paths, and through a number of simple additions significantly improves the prediction accuracy in larger networks. The improvement ratio compared to the baseline model is 101, from a 187.28% to a 1.828%, measured by the Mean Average Percentage Error (MAPE). In addition, we propose a closed-loop control context where the resulting GNN model could be potentially used in different use cases. [ABSTRACT FROM AUTHOR]

In this work, a soft sensor–based digital twin (DT) was developed to reduce the startup time in manufacturing plastic tubes and enable real-time product quality monitoring, i.e., the weight per unit length and the inner and outer diameters of the tube. An experimental campaign was conducted on a real tube extrusion line using three polyvinyl chloride (PVC) compounds and different process conditions, and machine learning regression algorithms were trained and tested to create the models of the extruder and the extrusion die the DT is based on. The characterization of the considered material, whose properties were given as input to the digital models, was carried out according to a procedure based only on the data collected by the production line. The DT was tested for the startup of the production of a single-layer tube and allowed to achieve the specified customer requirements (thickness and weight) in a few minutes. The proposed solution thus proved to be a valuable tool for reducing the setup time, thus increasing the efficiency of the process. [ABSTRACT FROM AUTHOR]

The increasing proportion of bifacial photovoltaic modules (Bi-PVM) in new projects makes the operation of photovoltaic system (PVS) more complicated, and it is difficult to accurately predict the power of the PVS. To solve this problem, this paper proposes a new power prediction method for PVS based on Bi-PVM. Firstly, the equal proportion digital twin model of the example project is constructed. The superposition principle is used to analyze the factors affecting the power generation performance of Bi-PVM, and the characteristic project is constructed according to the analysis results. Secondly, the bifacial correction coefficient is introduced to reduce the parameter error caused by Bi-PVM to the prediction model. On this basis, a power prediction machine learning model based on bidirectional gated recurrent unit (Bi-GRU) network is established. Finally, a simulation experiment is carried out on the TensorFlow machine learning platform. With the actual operation data of a PV power station in Jiuquan, China, the simulation analysis is carried out under four weather types, namely, sunny day, rainy day, snowy day and complex and changeable day, respectively, which verified the correctness and excellence of the proposed method. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing. [ABSTRACT FROM AUTHOR]

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research. [ABSTRACT FROM AUTHOR]

This paper presents a predictive computational framework for surrogate modeling of pressure field and optimization of pressure sensor placement for wind engineering applications. Firstly, a machine learning-derived surrogate model, trained by high-fidelity simulation data using finite element-based CFD and informed by a turbulence model, is developed to construct the full-field pressure from scattered sensor measurements in near real-time. Then, the surrogate pressure model is embedded in another neural network (NN) for optimizing pressure sensor placement. The goal of the NN-based optimizer is to learn the best layout of a fixed number of pressure sensors over the structural surface to deliver the most accurate full-field pressure prediction for various inflow wind conditions. We deploy the model to a representative low-rise building subjected to different wind conditions. The performance of the proposed framework is assessed by comparing the predicted results with finite element-based CFD simulation results. The framework shows excellent accuracy and efficiency, which could be potentially integrated with structural health monitoring to enable digital twins of civil structures. [ABSTRACT FROM AUTHOR]

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open-source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on Github. [ABSTRACT FROM AUTHOR]

In this article, a digital twin approach is proposed for modeling a pharmaceutical drying process using machine learning techniques, driven by data from different sensors captured in-line. The current difficulty with the drying process is mainly due to the manual operator control for choosing the end-point for terminating the drying step. This results in significant variability, depending on which human operator is supervising, and due to the multi-tasking nature of the job, generally allows a longer processing time to be sure the material is completely dry. The objective is to automate the end-point identification, thus optimizing the processing duration and therefore the overall energy consumption of the process. The point at which the drying is complete is indicated by the temperature difference between the ingoing and outgoing air flow. However, the stochastic nature of the process makes the data modeling a challenge. Firstly, a wide selection of supervised statistical and machine learning algorithms was benchmarked to find the one (CatBoost) which gave the best performance with the data. Next, the set of hyper-parameters was found for CatBoost which gave the optimum performance. This gave a best performance of 0.788 (R2) fitting of the drying end-point prediction with the real values, for a large number of batches (over 700 K records). This is considered a good result taking into account the high residual of data models for these data and the stochastic nature of the process. The approach has been deployed in a real setting digital twin to control the drying process cutoff in the production plant. The results show the viability of the approach for modeling the process and automatically identifying the optimum end-point for the drying process, thus achieving significant energy savings which have been quantified as approximately 3.7 MWh per year for the pharmaceutical company. [ABSTRACT FROM AUTHOR]

This paper develops an approach to improve a CNC machine's tool performance and slow down its degradation rate automatically in the Pre-Failure stage. A Deep Reinforcement Learning (DRL) agent is developed to optimize the machining process performance online during the Pre-Failure interval of the tool's life. The Pre-Failure agent that is presented in the proposed approach tunes the feed rate according to the optimal policy that is learned in order to slow down the tool's degradation rate, while maintaining an acceptable Material Removal Rate (MRR) level. The machine learning techniques and pattern recognitions are implemented to monitor and detect the tool's potential failure level. The proposed mechanism is applied to a CNC machine when turning Titanium Metal Matrix Composites (TiMMC). A CNC machine Digital Twin (DT) is developed to emulate the physical machine in the digital environment. It is validated with the physical machine's measurements. The proposed pre-failure mechanism is a model-free approach, which can be implemented in any machining process with fewer online computational efforts. It also validated on a wide range of cutting speeds, up to 15,000 RPM. Deployment of the proposed machine learning approach for the particular case study improves the tool's Time to Failure (T2F) by 40% and the MRR by 6%, on average, compared to the classical approach. [ABSTRACT FROM AUTHOR]

ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article's key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making. [ABSTRACT FROM AUTHOR]

An approach to the development of models for the digital description of the process of smelting a semi-finished product in an electric-arc furnace using machine-learning methods is presented. The capabilities and advantages of the models developed are shown considering, as an example, the oxidation (reduction) of metal impurities during smelting. [ABSTRACT FROM AUTHOR]

Purpose: Axial alignment of the talar implant in total ankle arthroplasty remains a major issue, since the real axis of motion of each patient is impossible to determine with usual techniques. Further knowledge regarding individual axis of motion of the ankle is therefore needed. Material and methods: Therefore, digital twins, artificial intelligence, and machine learning technology were used to identify a real personalized motion axis of the tibiotalar joint. Three-dimensional (3D) models of distal extremities were generated using computed tomography data of normal patients. Digital twins were used to reproduce the mobility of the ankles, and the real ankle of the patients was matched to the digital twin with machine learning technology. Results: The results showed that a personalized axis can be obtained for each patient. When the origin of the axis is the centre of mass of the talus, this axis can be represented in a geodesic system. The mean value of the axis is a line passing in first approximation through the centre of the sphere (with a variation of 3 mm from the centre of the mass of the talus) and through a point with the coordinates 91.6° west and 7.4° north (range 84° to 98° west; − 2° to 12° north). This study improves the understanding of the axis of the ankle, as well as its relationship to the possibility to use the geodesic system for robotic in ankle arthroplasty. Conclusion: The consideration of a personalized axis of the ankle might be helpful for better understanding of ankle surgery and particularly total ankle arthroplasty. [ABSTRACT FROM AUTHOR]