Summary of Latticeml: a Data-driven Application For Predicting the Effective Young Modulus Of High Temperature Graph Based Architected Materials, by Akshansh Mishra
LatticeML: A data-driven application for predicting the effective Young Modulus of high temperature graph based architected materials
by Akshansh Mishra
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Optimization and Control (math.OC); Applied Physics (physics.app-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LatticeML application uses machine learning to predict the effective Young’s Modulus of high-temperature graph-based architected materials. By leveraging finite element simulations and a range of regression models, including XGBoost Regressor, which achieved the highest accuracy, the framework can identify optimal designs and forecast performance for various material and geometric parameters. The study evaluates five supervised learning algorithms on eleven graph-based lattice structures with two high-temperature alloys, Ti-6Al-4V and Inconel 625. The application is deployed using Streamlit, enabling users to interactively input parameters and receive predicted Young’s Modulus values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new tool that helps design and test special materials called architected materials. These materials have unique shapes and properties that can be adjusted for specific uses. The tool, called LatticeML, uses machine learning to predict how well these materials will work under different conditions. It tested 11 different designs and two types of strong alloys to see which ones worked best. By using this tool, engineers can design new materials that are stronger or more efficient. |
Keywords
» Artificial intelligence » Machine learning » Regression » Supervised » Temperature » Xgboost
