A Novel Approach to Discrete Representative Volume Element Automation and Generation-DRAGen.

Area of Science:

• Materials Science
• Computational Materials Science
• Microstructure Engineering

Background:

• Generating Representative Volume Elements (RVEs) is crucial for accurate microstructural simulations.
• Existing RVE generators often lack modularity and simplicity, hindering extensibility.
• Realistic reconstruction of complex microstructures, like those with elongated grains or bands, remains a challenge.

Purpose of the Study:

• Introduce a novel, modular, and simple RVE generator based on discrete methods.
• Implement the generator as a Python tool for practical application.
• Demonstrate the capability to reconstruct realistic microstructures from input data.

Main Methods:

• Development of a Python-based tool for RVE generation.
• Utilizing discrete methods for synthetic microstructure reconstruction.
• Implementation of a Random Sequential Addition (RSA)-Algorithm for discrete volumes.
• Employing a discrete tessellation function for microstructure realization.

Main Results:

• Successful reconstruction of realistic synthetic microstructures.
• Generation of microstructures featuring elongated grains and martensite bands.
• Validation of the generator's ability to use input data sets effectively.

Conclusions:

• The novel discrete RVE generator offers a simple, modular, and extensible solution.
• The Python tool successfully generates complex microstructures essential for simulations.
• This approach enhances the fidelity of microstructural modeling.