4,411 research outputs found
Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework
Materials-by-design has been historically challenging due to complex
process-microstructure-property relations. Conventional analytical or
simulation-based approaches suffer from low accuracy or long computational time
and poor transferability, further limiting their applications in solving the
inverse material design problem. Here, we establish a deep learning and genetic
algorithm framework that integrates forward prediction and inverse exploration.
This framework provides an end-to-end solution to achieve application-specific
mechanical properties by microstructure optimization. In this study, we select
the widely used Ti-6Al-4V to demonstrate the effectiveness of this framework by
tailoring its microstructure and achieving various yield strength and elastic
modulus across a large design space, while minimizing the stress concentration
factor. Compared with conventional methods, our framework is efficient,
versatile, and readily transferrable to other materials and properties. Paired
with additive manufacturing's potential in controlling local microstructural
features, our method has far-reaching potential for accelerating the
development of application-specific, high-performing materials.Comment: 19 pages, 5 figure
- …