| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
Multi-Modal Machine Learning Framework for Property Prediction in Ni-Based Superalloys and Aluminum Alloys Using Integrated Characterization Data |
| Author(s) |
Jiwon Park, Chang-Seok Oh |
| On-Site Speaker (Planned) |
Jiwon Park |
| Abstract Scope |
Multi-modal machine learning approaches have emerged as powerful tools for integrating diverse materials characterization data to advance materials discovery and optimization. In this study, we present a novel framework that combines microstructural images, X-ray diffraction patterns, compositional data, and processing parameters to predict properties of Ni-based superalloy and aluminum alloys. Our model architecture employs parallel neural network branches to process each data modality independently before fusion: convolutional neural networks for microstructure image analysis, XRD data without peak and phase assessments, and fully connected layers for composition and process variables. The fused representation enables both property prediction and interpretable feature importance analysis across modalities. This work demonstrates the potential of multi-modal learning to leverage complementary materials characterization techniques for enhanced materials informatics and accelerated alloy development. |
| Proceedings Inclusion? |
Undecided |