New Method for Predicting Yield Strength of Wrought Aluminum Alloys ID: 2024-054
A cutting-edge Python-based method for accurately predicting the yield strength of wrought aluminum alloys from their composition.
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Technology Overview
This innovative technology offers a predictive model for the yield strength of wrought aluminum alloys by analyzing their composition. Developed at Brigham Young University, the method facilitates the exploration of new alloy compositions beyond the standard series, leveraging Thermo-Calc for input data and implemented in Python. It is designed to aid alloy developers in creating both heat-treatable and non-heat-treatable alloys with enhanced properties.
Key Advantages
- Applicable across multiple aluminum series, including non-standard compositions
- Ability to incorporate new strengthening phases as discovered, enhancing predictive accuracy
- Utilizes CALPHAD software for estimating crucial properties for precipitation strengthening
- Supported by NSF funding, indicating robust external validation
Problems Addressed
- Enables the exploration of new alloy compositions beyond typical elemental ranges
- Overcomes limitations of existing models by spanning multiple series and incorporating new phases
- Facilitates accurate prediction of alloy strength, crucial for material development and application
Market Applications
- Alloy development and optimization for industrial applications
- Research tool in materials science for studying aluminum alloy properties
- Potential collaboration with aluminum manufacturers like Novelis for tailored alloy production
Additional Information
Technology ID: 2024-054
Sell Sheet: Download the Sell Sheet here
Market Analysis: Contact us for a more in-depth market report
Date Published: 07 May, 2025
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