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

ABSTRACT: 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 geome

SUBMITTER: Negozio M 

PROVIDER: S-EPMC11923085 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature