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Aluminum alloy

vh's picture

Prediction of forming limit diagrams using machine learning

Measuring forming limit diagrams (FLDs) is a time consuming and expensive process. Machine learning (ML) methods are a promising route to predict FLD of aluminium alloys. In the present work, we developed a machine learning (ML) based tool to establish the relationships between alloy composition / thermomechanical processing route to the material's FLD.

vh's picture

Call for Abstracts: Numisheet 2020 mini symposium on “Challenges and Opportunities in Forming Aluminum”

The NUMISHEET conference series is the most significant international conference on the area of the numerical simulation of sheet metal forming processes. Within Numisheet 2020, we are organizing a mini symposium on “Challenges and Opportunities in Forming Aluminum”.

Amit Pandey's picture

Negative to positive strain rate sensitivity in 5xxx series aluminum alloys

In continuation to our previous work on 5xxx series Al alloys

Experimental and numerical investigations of yield surface, texture, and deformation mechanisms in AA5754 over low to high temperatures and strain rates
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