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| Url | https://cimne.com/sgp/rtd/Project.aspx?id=1021 | ||
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| Acronym | DT-FSW | ||
| Project title | Digital Twin for High-Performance Components Production via Friction Stir Welding Process | ||
| Reference | PID2023-147968OB-I00 | ||
| Principal investigator |
Narges DIALAMISHABANKAREH - narges@cimne.upc.edu
Michele CHIUMENTI - michele@cimne.upc.edu |
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| Start date | 01/09/2024 | End date | 31/08/2027 |
| Coordinator | CIMNE | ||
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| Program | P.E. para Impulsar la Investigación Científico-Técnica y su Transferencia | Call | Proyectos Generación de Conocimiento 2023 |
| Subprogram | Subprograma Estatal de Generación de Conocimiento | Category | Nacional |
| Funding body(ies) | MCIU | Grant | $173,750.00 |
| Abstract | The present proposal aims at developing an approach for merging sensor data into a numerical model to create a Digital Twin (DT) for industrial manufacturing processes. This is particularly crucial for joining high-performance engineering components, where the choice of welding technology significantly impacts the creation of durable bonds. As product quality standards become stricter, the complexity of production for achieving higher strength, temperature stability, and corrosion resistance increases. From this perspective, Friction Stir Welding (FSW), being a solid-state welding technique, has been one of the most promising options. However, the quality of FSW joints depends on numerous operational parameters, presenting challenges for selecting optimal process parameters, which is particularly challenging when working with new materials. To address this, the proposal aims to establish a methodology capable of selecting operating parameters and work designs tailored to specific workpiece materials, thicknesses, and geometries to efficiently achieve highquality joints. Due to complex requirements when designing the FSW process it is essential to perform preliminary analysis experimental plans, production of an experimental tool batch tool design improvements, endurance tests. The production process of the tool batch is lengthy and expensive, and specific to a FSW case. In this context establishing a DT for FSW process design can be strongly beneficial for reducing preproduction time and expanding the range of products. Transforming FSW processes through the integration of cutting-edge technologies is the central objective, facilitating real-time calculations. This can be achieved by employing Artificial Neural Networks (ANN) trained with preprocessed datasets and the reduced basis method. By leveraging the capabilities of these advanced techniques, the goal is to enhance the efficiency, accuracy, and adaptability of FSW, creating a more responsive and intelligent welding system that meets the demands of dynamic manufacturing environments. In particular, the DT will facilitate finding the optimal set of process parameters and pin-tool designs for producing defect-free and high-performance components. The justification for the DT-FSW lies in its potential to significantly improve quality, reduce costs, and enhance operational efficiency. Providing a platform for innovation and research, it fosters continuous improvement in manufacturing processes, that can be always enhanced further by feeding it with newly available experimental or simulation data. The predictive analytics and virtual experimentation aspects not only lead to cost savings but also minimize risks associated with defects, rework, and production delays. By offering a comprehensive solution for monitoring, simulation, and optimization, the proposal underscores its value in advancing industrial manufacturing, particularly in complex FSW processes. | ||
| Proyecto PID2023-147968OB-I00 financiado por MCIU/AEI/10.13039/501100011033/ FEDER, UE | |||