| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Friction Stir Welding and Processing XIII
|
| Presentation Title |
Optimizing Hardness in the Heat Affected Zone of AA7075-T6 Aluminum
Alloy via Machine Learning-Guided Friction Stir Welding |
| Author(s) |
Yizhou Lu, Shubhrodev Bhowmik, Nilesh Kumar, Samrat Choudhury |
| On-Site Speaker (Planned) |
Yizhou Lu |
| Abstract Scope |
Friction stir welding (FSW) of precipitation-strengthened aluminum alloys remains to be a
challenging problem due to a significant reduction in strength or hardness within the heat-
affected zone (HAZ). The hardness in the HAZ is affected by various parameters, including tool
rotational rate, tool traverse speed, and tool shoulder diameter among others. In this study, we
utilized machine learning tools to identify the key processing parameters that determine the
hardness in the HAZ of AA7075-T6 alloy. Subsequently, we employed an adaptive design
strategy to iteratively identify the combination of processing parameters required to achieve a
targeted hardness in the HAZ. The goal is to maximize the improvement in targeted hardness
with each subsequent experiment until the desired hardness is achieved. Our machine learning
guided approach revealed that only a small set of data needs to be generated experimentally to
attain the targeted hardness in the HAZ. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Aluminum, Joining, Machine Learning |