In situ conductometry for studying the homogenization of Al-Mg-Si alloys and predicting extrudate grain structure through machine learning

In industrial practice, no sensors capable of obtaining microstructural information in situ during thermo-mechanical processing of Al alloys are commonly employed. Inductive electrical conductivity measurement is safe, inexpensive, and capable of acquiring valuable information about precipitation and dissolution processes. However, commercial eddy current sensors work only at low temperatures near room temperature and are thus not suitable for in situ conductometry during heat treatments of Al alloys. We designed a high-temperature eddy current sensor and performed in situ conductometry during the homogenization of six Al-Mg-Si wrought alloys, three of which are experimental recycling-friendly alloys with increased Fe content. The results are interpreted with regard to microstructural investigations, and the advantages and limitations of our approach are discussed. As a proof-of-concept, we show how the conductivity curves and extrusion process parameters can be combined to predict final extrudate grain structures using machine learning. To achieve this, we employed finite element simulation of extrusion coupled with microstructural simulation over a wide parameter range, validated by extrusion experiments and metallography, and trained a feedforward neural network. We believe our interdisciplinary approach can lead to improvements in the industrial processing of Al wrought alloys.