Tailoring the mechanical properties of 3D microstructures: a deep learning and genetic algorithm inverse optimization framework

Xiao Shang

email: [email protected]

We report a framework that 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.

Core dependencies and librarys

• MATLAB python engine R2023a
• Neper 4.4.2 - 33 (https://neper.info/) A free / open source software package for polycrystal generation and meshing.
• Numpy 1.23.5
• Tensorflow 2.10.1
• Pandas 1.5.3
• PyGAD 2.18.1 (https://pygad.readthedocs.io/en/latest/) An open-source Python library for building the genetic algorithm.
• Pymoo 0.6.0.1 (https://pymoo.org/) An open-source framework for state of the art single- and multi-objective optimization algorithms.
• python 3.9.16
• Scikeras 0.9.0
• Sklearn 1.0.2

How to use

1. Make sure your have all required dependencies at the correct versions.
2. Clone this repo to your local directory uisng git clone https://github.com/xshang93/MsInverseDesign/
3. In the GA_main_final.py file, line 204, select the ojb_select to be either 4 or 5. 4 is for maximizing both strength and elastic modulus, and 5 is for maximizing stength but minimizing modulus. Both minimizes the stress concentration factor.
4. Wait for the optimization loop to converge. The results and history will be stored in a folder named opt_run.