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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title Enhancing Extrusion Efficiency: Development of a Digital Twin for Glass Reinforced Polymer Processes Using Machine Learning and Real-Time Data Integration
Author(s) Gulshan Noorsumar, Sayan Adhikari, Hallvard Gustav Fjær, Øyvind Jensen, Michaela Meir
On-Site Speaker (Planned) Gulshan Noorsumar
Abstract Scope This paper presents the development of a digital twin for an extruder, leveraging advanced modeling and computational techniques to enhance the efficiency and reliability of extrusion processes. The digital twin integrates real-time data acquisition, machine learning (ML) algorithms and physics-based models to create a comprehensive virtual representation of the extruder. The paper focuses on the simulation models developed to replicate the extrusion process of thermal sheets from glass fibre reinforced polymer material along with the data generation to make surrogate models for the digital twin. A neural network (NN) based model was developed to simulate the extrusion process within a digital twin framework, utilizing real-time sensor data to predict defects in thermal sheets. Additionally, we investigate the potential benefits and challenges of deploying digital twins in industrial settings, and we explore the possibility of optimizing energy usage for such energy-intensive processes.
Proceedings Inclusion? Planned:

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