Multi-Scale Analyses and Modeling of Metallic Nano-Layers

Area of Science:

• Materials Science
• Computational Mechanics
• Nanotechnology

Background:

• Metallic nano-layers are crucial in advanced applications, but their thermomechanical behavior is complex.
• Simulating these behaviors requires robust multi-scale models that bridge nano- and macro-scopic phenomena.
• Existing models often face challenges in accuracy, computational cost, and scalability.

Purpose of the Study:

• To develop and validate multi-scale computational models for predicting the thermomechanical responses of metallic nano-layers.
• To establish accurate constitutive models at both nano- and homogenized scales.
• To enhance the efficiency of large deformation finite element simulations for crystal plasticity.

Main Methods:

• Developed a size-dependent constitutive model at the nano-scale using entropic kinetics and a deep-learning technique (single layer calibration).
• Created a homogenized crystal plasticity-based constitutive model using statistical analyses and metaheuristic genetic algorithms.
• Incorporated temperature-dependent properties into the homogenized model for comprehensive analysis.

Main Results:

• The nano-scale model accurately predicted behavior validated against experimental data, showing sensitivity to size, loading, and geometry.
• The homogenized model significantly accelerated computations (orders of magnitude) while maintaining accuracy, verified with nano-scale data.
• The temperature-dependent homogenized model was validated experimentally, demonstrating its capability to analyze thermal effects.

Conclusions:

• The developed multi-scale framework provides accurate and efficient simulation tools for metallic nano-layer thermomechanical responses.
• The single layer calibration and genetic algorithms offer effective parameter acquisition strategies.
• The models pave the way for advanced design and analysis of nanostructured materials under various conditions.