Predicting Flow Stress Behavior of an AA7075 Alloy Using Machine Learning Methods

Predicting Flow Stress Behavior of an AA7075 Alloy Using Machine Learning Methods

The present work focuses on the prediction of the hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. The data considered focus on a novel hot forming process at different tool temperatures ranging from <inline-formula><math xmlns="...

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Bibliographic Details
Main Authors: Jens Decke, Anna Engelhardt, Lukas Rauch, Sebastian Degener, Seyed Vahid Sajadifar, Emad Scharifi, Kurt Steinhoff, Thomas Niendorf, Bernhard Sick
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Crystals
Subjects:
machine learning
eXtreme Gradient Boosting
Zerilli–Armstrong
flow stress
AA7075
Online Access:https://www.mdpi.com/2073-4352/12/9/1281

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