Smart extrusion via data-driven prediction of grain size and peripheral coarse grain defect formation.

Optimization of the mechanical and crash properties of extruded AA6XXX automotive profiles requires detailed understanding of grain size evolution and formation of the peripheral coarse grain (PCG) defect. Conventional approaches demand extensive experimental campaigns which, further to being lengthy and resource-intensive, often lead to the adoption of conservative process parameters that limit production efficiency. The present work proposes a data-driven method for predicting grain size and PCG formation, combining finite element method (FEM) simulation data with experimental microstructural observations to train machine learning (ML) algorithms for classification and regression. Over 22,000 data points were collected from two distinct AA6082 aluminum alloy profiles with different geometries, extruded over a range of different process conditions. Hyperparameter optimization of artificial neural networks (ANNs) for regression and prediction was performed, with ANN outcomes benchmarked against existing analytical models. PCG formation was predicted with 5.8% false negatives and 3.9% false positives compared to 10.1% and 18.1%, respectively, with existing analytical models, while grain size was predicted with a mean squared error (MSE) of 9.77 μm2 compared to 48.3 μm2. PCG and grain size maps were then produced to demonstrate how prediction of grain size and PCG formation can be employed in a smart manufacturing environment.