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Decoupling the physical world and providing standardized service interfaces is still challenging when developing Location Based Services (LBS). This lack also hinders the possibility of developing Intelligent services on top of LBS architectures. In this paper, we propose a multi-layer Digital Twin-based architecture that aims to enable the development of machine learning-based Intelligent LBS (I-LBS) that are able to adapt, evolve, and perform Continual Learning (CL). The platform uses Digital Twins to ensure physical abstraction and provide cyber–physical knowledge to the I-LBSs, which is defined as an execution graph of operation modules. Finally, we simulated a use-case for this platform in the complex scenario of Healthcare organization and management where the I-LBS classifies allowed/not allowed trajectories of users inside a real-existing hospital scenario depending on their role in the organization. The use case is implemented as a Deep Learning-based reconstruction task of high-resolution trajectories processed by the DT architecture that also deploys the I-LBS. The platform is evaluated in terms of physical complexity and computational time on the DT side and on both a traditional machine learning setting and a replay-based CL one for the intelligence side to demonstrate the flexibility and adaptability features introduced by the components for dynamic or unseen scenarios. [ABSTRACT FROM AUTHOR]

Early failure detection is required for Fused Deposition Modelling (FDM) 3D printers to reduce material waste. Typically, such systems are created based on images captured during printing or sensor data for tracking the extruder's movement. This work presents a novel approach to sensor data-driven fault diagnosis, utilizing Artificial Intelligence (AI) technology to investigate the temperature imbalance in the extruder and printing surface. First, a Lightweight Convolutional Neural Network (LCNN) is proposed to detect faults from sensory data. The model's architecture concatenates the CNN layer to extract additional features, improving the model's performance while maintaining a lightweight configuration suitable for real-time monitoring systems. Second, the concept of Digital Twin (DT) technology for FDM 3D printer fault detection is introduced. The DT creates a virtual representation of a physical object, and its functionality is validated by examining the network's latency and System Overhead (SO) as the number of clients increases. The simulation results show that the proposed LCNN with a DT environment can effectively monitor, detect, and control the physical workplace with an F1-Score of 0.9981 and an average latency of 995. 4253 ms. Additionally, this research contributes to the development of future technologies for virtual condition monitoring of 3D printer abnormalities, which will be essential for intelligent and autonomous factories. [ABSTRACT FROM AUTHOR]

Finite element method (FEM) based high-fidelity simulation can be computationally demanding and time-consuming as engineering problems become more complicated. It is thus necessary to develop a surrogate model that only requires a small amount of computational time but retains sufficient accuracy. A graph neural network (GNN) based framework is proposed as a general surrogate model for FEM to simulate the Von Mises stress distribution. The mesh body is embedded to a graph and a novel global attribute representation is introduced to capture the geometry and boundary conditions while overcoming the common problem of over smoothing in graph deep learning. The challenge to deal with varying geometry and boundary conditions is overcome by the proposed model and thus it outperforms existing methods such as proper orthogonal decomposition (POD) and greedy algorithm in terms of generalization. Numerical experiments are given to demonstrate the capability of the model developed and the results show that the proposed model not only accurately predicts the stress distribution but also speed-ups hundreds of times faster compared to a FEM-based simulator, enabling real-time structural response analysis for the application of digital twin and structural health monitoring. It is indicated that GNNs can be a powerful tool for resolving complex physical problems, thereby assisting in advancing science and enhancing engineering productivity. • A surrogate model is developed to accelerate the numerical simulation. • Global attributes are proposed to mitigate potential issues with graph deep learning. • Accurate prediction of physical quantities of interest is achieved. • Significant improvements in prediction speed over numerical simulation are achieved. • Powerful generalizability is obtained through the use of graph neural networks. [ABSTRACT FROM AUTHOR]

The paper proposes a surrogate model for liquid moulding of structural composites. A methodology is presented to simulate the dual-phase Darcy's flow in a heterogeneous porous medium. The approach is an encoder–decoder that receives as input a matrix of permeabilities and produces two scalar fields that represent the pressure and front flow. This model is trained with synthetic data generated with a computer fluid dynamics simulator. In this context, the lack of robustness of models trained with the popular L 2 and L 1 losses is highlighted and several enhancements to these baseline approaches are introduced. First, the study provides a piece-wise power-logarithmic loss that improves training in the presence of the bimodal distribution of error residuals produced by the dual-phase flow predictions. A non-uniform sampling strategy for the selection of time training snapshots is also included, which contributes to improve the prediction accuracy. The estimation of the front flow field is further refined with a multi-task training strategy. The introduction of these improvements in the baseline models reduce the relative error of the pressure and front flow fields by more than 50%, performing these simulations in a record time of 50 ms. The surrogate model is further evaluated as a digital twin to predict – in a real experiment – the location and spatial extent of race-tracking channels and regions with dissimilar degrees of permeability. [ABSTRACT FROM AUTHOR]