EventsThe 5th International Conference on Materials: Advances in Material Innovation
Published
This submission belongs to the session S1. Materials Characterization, Processing and Manufacturing of the event The 5th International Conference on Materials: Advances in Material Innovation
Published date
25 Sep, 2024
Academic Editor
author-avatarMaryam Tabrizian
Citation
Bharat Gwalani, Multi-Stimuli Integration in Alloy Design: A Shear-Assisted Processing Approach for High-Performance Nano-Composite Materials, in Proceedings of The 5th International Conference on Materials: Advances in Material Innovation, Basel, 25 September–27 September 2024, MDPI: Basel, Switzerland
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Multi-Stimuli Integration in Alloy Design: A Shear-Assisted Processing Approach for High-Performance Nano-Composite Materials

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1. North Carolina State University, USA
Abstract

Typically, in alloy design, thermodynamic modeling is employed to foresee equilibrium states. This is succeeded by thermomechanical treatment to disrupt the equilibrium state, followed by final annealing treatments to achieve a consistently stable state for practical applications. However, this conventional approach considers thermal activation, stress, and chemical stress separately, often resulting in states that may not be the most efficient utilization of precious metallic ingredients. Our research aims to integrate multiple stimuli, such as mechanical stress, chemical potentials, and thermal activation, to create multifunction composite alloys. In this presentation, I will share examples of how this approach was effectively applied in our ongoing research. For example, we utilize an Al-Mg alloy modified with Fe3O4 particles through friction-assisted processing to produce nano-composite materials comprising Al, Al-Fe intermetallics, core-shell particles of Fe+MgO, and Al2O3. These composites exhibit high mechanical strength, ductility, and ferromagnetic characteristics.

Keywords
Solid State Processing
Characterization
Mechanical Properties
Magnetism
Advances in Preparation and Characterization of Polymer Nanocomposites
Characterising Liquid Crystals via Machine Learning