Smart manufacturing, tailored by the 4th industrial revolution and forces like innovation, competition, and changing demands, lies behind the concurrent evolution (also known as co-evolution) of products, processes and production systems. Manufacturing companies need to adapt to ever-changing environments by simultaneously reforming and regenerating their product, process, and system models as well as goals and strategies to stay competitive. However, the ever-increasing complexity and ever-shortening lifecycles of product, process and system domains challenge manufacturing organization's conventional approaches to analysing and formalizing models and processes as well as management, maintenance and simulation of product and system life cycles. The digital twin-based virtual factory (VF) concept, as an integrated simulation model of a factory including its subsystems, is promising for supporting manufacturing organizations in adapting to dynamic and complex environments. In this paper, we present the demonstration and evaluation of previously introduced digital twin-based VF concept to support modelling, simulation and evaluation of complex manufacturing systems while employing multi-user collaborative virtual reality (VR) learning/training scenarios. The concept is demonstrated and evaluated using two different wind turbine manufacturing cases, including a wind blade manufacturing plant and a nacelle assembly line. Thirteen industry experts who have diverse backgrounds and expertise were interviewed after their participation in a demonstration. We present the experts' discussions and arguments to evaluate the DT-based VF concept based on four dimensions, namely, dynamic, open, cognitive, and holistic systems. The semi-structured conversational interview results show that the DT-based VF stands out by having the potential to support concurrent engineering by virtual collaboration. Moreover, DT-based VF is promising for decreasing physical builds and saving time by virtual prototyping (VP).
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http://dx.doi.org/10.1007/s00170-021-06825-w | DOI Listing |
Accid Anal Prev
March 2025
School of Information Science and Technology, ShanghaiTech University, Shanghai, China; Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai, China. Electronic address:
Advanced Driver Assistance Systems (ADAS) are crucial for enhancing driving safety by alerting drivers to unrecognized risks. However, traditional ADAS often fail to account for individual decision-making processes, including drivers' perceptions of the environment and personal driving styles, which can lead to non-compliance with the provided assistance. This paper introduces a novel Cognitive-Digital-Twin-based Driving Assistance System (CDAS), leveraging a personalized driving decision model that dynamically updates based on the driver's control and observation actions.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of Automation, "Dunărea de Jos" University of Galați, 800008 Galați, Romania.
The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates modern principles of smart manufacturing as outlined in Industry/Education 4.0 (automation, data exchange, smart systems, machine learning, and predictive maintenance) and Industry/Education 5.
View Article and Find Full Text PDFMaterials (Basel)
November 2024
Engineering Faculty, Mondragon Unibertsitatea, Loramendi 4, 20500 Arrasate-Mondragon, Spain.
Broaching is a key manufacturing process that directly influences the surface integrity of critical components, impacting their functional performance in sectors such as aeronautics, automotive, and energy. Such components are subjected to severe conditions, including high thermomechanical loads, fatigue, and corrosion. For this reason, the development of predictive models is essential for determining the optimal tool design and machining conditions to ensure proper in-service performance.
View Article and Find Full Text PDFFront Med (Lausanne)
October 2024
Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Gistrup, Denmark.
Background: Acute respiratory distress syndrome (ARDS) is highly heterogeneous, both in its clinical presentation and in the patient's physiological responses to changes in mechanical ventilator settings, such as PEEP. This study investigates the clinical efficacy of a physiological model-based ventilatory decision support system (DSS) to personalize ventilator therapy in ARDS patients.
Methods: This international, multicenter, randomized, open-label study enrolled patients with ARDS during the COVID-19 pandemic.
Sensors (Basel)
September 2024
Design and Precision Engineering, IDEKO, 20870 Elgoibar, Spain.
Machine tool accuracy is greatly influenced by geometric and thermal errors that cause positioning deviations within its working volume. Conventionally, these two error sources are treated separately, with distinct procedures employed for their characterization and correction. This research proposes a unified volumetric error compensation approach in terms of a calibration procedure and error compensation model, which considers geometric and thermal errors as a single error source that exhibits temporal variation primarily due to changes in the machine's thermal state.
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