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Meeting TMS Specialty Congress 2025
Symposium Joint Sessions of AIM, ICME, & 3DMS
Presentation Title Manufacturing and Control of Fiber Reinforced Polymer Composites Through FMEA-Based Digital Twin
Author(s) Arshdeep Singh, Soban Babu Beemaraj, Sooriyan Senguttuvan, Amit Salvi
On-Site Speaker (Planned) Soban Babu Beemaraj
Abstract Scope Manufacturing high-quality polymer composite parts without rejection is critical, especially for aerospace and wind energy structures requiring precise temperature control. This paper presents a Failure Mode and Effect Analysis (FMEA) framework for composite manufacturing, implemented through a digital twin that monitors and controls the process. Using this digital twin, multiple process deviations and potential failure scenarios in composite manufacturing are modeled, and optimal corrective actions are computed to create the FMEA table. This digitally generated FMEA table is then used to detect anomalies in the physical manufacturing process. To simulate this process, a multi-scale cure kinetics model is developed to capture the part's thermo-chemical-mechanical state. Additionally, the Joule heating effect is modeled for resistive heating elements embedded in a PID-controlled molding tool. Demonstrated on a tapered laminated composite with multiple heating zones, this approach enhances quality control and ensures more efficient composite manufacturing.
Proceedings Inclusion? Definite: Post-meeting proceedings

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Efficient, Coupled Process-Structure-Property Simulations of Additive Manufacturing Using the “Materialize” Framework
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FactoryNet: A Labeled Image Dataset for the Manufacturing Environment
FIB-SEM Serial Sectioning Tomography: Towards 24-Hour Time-to-Results
Generalized Graph Foundation Models as Versatile Data-Driven Digital Twins for Complex Technological Systems
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Influence of 3D Crack Networks for High Toughness Responses in Tantalum Carbides
Innovations in 3D EBSD for Advanced Materials Characterization
Manufacturing and Control of Fiber Reinforced Polymer Composites Through FMEA-Based Digital Twin
Materials Microstructure Design Integrated With Image-Based Simulation
Modular and Interoperable Materials Data Science Ontology (MDS-Onto) for Knowledge Graphs and Semantic Reasoning
NIMS's Data-Driven Materials Research Platform: Enhancing MLOps With Literature-Based Data Integration
Pinax: A Machine Learning Platform for Data-Driven Materials Development
Smart Sustainable Packaging for Local Fruits—TRACE Your Food, KNOW Your Food, TAKE CARE of Trash
The Materials Science and Engineering Knowledge Graph: Establishing a Centralized Metadata Index for Enhanced Data Integration
Toward Sentient Manufacturing
Towards Structured Data Spaces: Prototypical Application of Semantic Technologies as a Driver for Innovation in Materials Science
Transforming Materials Science With Concepts for a Semantically Accessible Data Space
Uncertainty Quantification, Error Propagation, and Sensitivity Analysis for Synchrotron X-Ray Residual Stress Measurements
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X-Ray Diffraction Analysis Using TensorFlow and FAIR Data Pipelines

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