Abaqus_Voronoi_toolbox

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Abaqus_Voronoi_toolbox

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# Abaqus_Voronoi_toolbox
If you are using this tool in your work, please cite the following paper:

Zhou S, Ben Bettaieb M, Abed-Meraim F (2026) Flexible Generation of Polycrystalline RVEs on Pre‑Meshed Geometries: A Novel ABAQUS Toolbox for Crystal Plasticity Simulations. Integrating Materials and Manufacturing Innovation. https://doi.org/10.1007/s40192-026-00466-1

# Update information
28-07-2026： add lines to remove the random/user-defined seeds added to the nodes in .inp file.

01-08-2026： 
1) add seed points output for the general module (seed_points.csv in your work directory)

2) add the uniform grains module (see below)
             
<img width="1828" height="675" alt="2026-08-01_074746" src="https://github.com/user-attachments/assets/e558cd22-ea9d-42d1-a220-09b5fa4e9dd8" />

08-08-2026：add Periodic Grains module

14-08-2026： add one element one grain module
<img width="639" height="350" alt="2026-08-14_120404" src="https://github.com/user-attachments/assets/491f9bc9-ac96-4852-9a0d-b2eead371908" />

upcoming: grain boundary

# Usage instructions

The developed toolbox supports ABAQUS versions ranging from 6.14 to 2022. A key advantage of the developed toolbox is its seamless integration into the standard ABAQUS/CAE workflow as a plug-in. The generation of polycrystalline microstructures becomes therefore straightforward and can be summarized in three main steps:

• Create the model geometry within the ABAQUS ‘Part’ module, or import it from external CAD software.

• Discretize the geometry using the ABAQUS ‘Mesh’ module, or import the mesh file from external software (if the geometry is already discretized, Step 1 can be skipped).

• Launch the ABAQUS-Voronoi toolbox from the Plug-ins menu (see the following figure), specify the required parameters, and generate the Voronoi microstructure.

# Compare with NEPER software
## The RVE model without void

NEPER command:

• neper -T -n 200 -domain 'cube(1,1,1)' -morphooptiini "coo:file(random_points.txt)" -o ccc  #random_points.txt in the download folder

• neper -M ccc.tess -elttype hex -rcl 0.91

• neper -V ccc.msh -dataelsetcol id -showelset "(id==1)||(id==120)" -cameraangle 15 -imagesize 1200:800 -print image_xxc

• neper -V ccc.tess,ccc.msh -dataelsetcol id -imagesize 1200:800 -print image_18

## The RVE model with void
<img width="1010" height="459" alt="ScreenShot_2026-04-17_153233_512" src="https://github.com/user-attachments/assets/efd110ea-2cef-45ba-a0c1-f5a727ec5a2f" />

# Advanced RVEs generated using the developed toolbox

## 1 RVE with a gradient structure
Gradient-structured metals represent an emerging class of advanced materials, characterized by outstanding combination of strength and ductility (Li et al., 2017; Zhao et al., 2021; Liu et al., 2024). Accurately modeling such functionally graded microstructures remains a significant challenge, for which the user-defined module of the developed toolbox is particularly suitable. The following figure illustrates an example of a gradient-structured RVE, with fine grains at the top transitioning to coarse grains at the bottom. The generation of this complex RVE can be achieved through a straightforward two-step workflow:

• Generate gradient seeds: define a set of seed coordinates with a prescribed spatial distribution. The Python script below demonstrates how to vary the seed density along the \mathit{\mathrm{Z}}-axis to produce the desired gradient.
• Import seeds into the toolbox: the generated seed coordinates are imported into the user-defined module. After verifying all inputs within the module, the final RVE is generated.

```python
import numpy as np
np.random.seed(42)
num_points = 1000 #the number of seeds
B = np.zeros((num_points, 3))
B[:, 0] = np.random.rand(num_points)
B1 = np.abs(np.random.randn(num_points))
B[:, 2] = 1 - B1 / np.max(B1)
B[:, 1] = np.random.rand(num_points)
np.savetxt('points.txt', B)
```

## 2 RVE with a rough surface
Surface quality and roughness are critical factors influencing component performance, particularly in localized necking predictions (Liu et al., 2020; Bong and Lee, 2021; Liu et al., 2022). Modeling these features is essential, but remains a major challenge for conventional RVE generators, which create the mesh and grain structure simultaneously. The following figure shows a polycrystalline RVE with a rough top surface. The generation of this complex RVE has been carried out in two steps:

• The open-source ABAQUS plug-in RufGen (Lim and Ha, 2023) has been used to generate a finite element model with a controlled rough surface. Here, the surface roughness parameters are set to: Lx=Ly=Lz=1, Le=0.03, Nx=Ny=100, and std=0.005.

• The general module of the developed toolbox has been applied directly to the pre-meshed rough model to partition it into 200 grains.

This example further highlights the flexibility and reliability of the proposed toolbox in partitioning the pre-existing meshes with complex geometric features.

## 3 Cylindrical RVE

Cubic RVEs are commonly used, while cylindrical RVEs are also widely employed in various engineering simulations Tang et al. (2023). Generating polycrystalline microstructures within such non-cubic geometries remains a challenge for many RVE generation tools. This example demonstrates how the user-defined module of the developed toolbox can efficiently handle this task. The following figure illustrates a cylindrical RVE composed of 200 grains. The generation procedure involves three steps:

• The desired cylindrical part is created and meshed using standard tools within ABAQUS/CAE.

• A custom script is used to generate seed coordinates, randomly distributed within the cylindrical volume. A Python template for this procedure is provided below.

```python
import numpy as np
np.random.seed(42)
radius = 5 #the radius of the cylinder      
height = 20 #the height of the cylinder     
num_points = 200 #the number of seeds
center = (5, 5) #the center of the cylinder
points = []
while len(points) < num_points:
    x = np.random.uniform(center[0] - radius, center[0] + radius)
    y = np.random.uniform(center[1] - radius, center[1] + radius)  
    if (x-center[0])**2 + (y-center[1])**2 <= radius**2:
        z = np.random.uniform(0, height)
        points.append([x, y, z])
points = np.array(points)
np.savetxt('cylinder_points.txt', points)
```
• The file containing the seed coordinates is imported into the user-defined module, which applies the Voronoi tessellation directly onto the pre-existing cylindrical mesh.

# Reference

Li J, Weng GJ, Chen S, Wu X (2017) On strain hardening mechanism in gradient nanostructures. Int J Plast. 88:89–107. https://doi.org/10.1016/j.ijplas.2016.10.003

Zhao F, Xie J, Zhu Y, Liu Q (2021) A novel dynamic technique to form reverse grain size gradient microstructure in pure copper. Mater Lett. 291:129581. https://doi.org/10.1016/j.matlet.2021.129581

Liu CZ, Xiong QL, Wen A (2024) Shear localization in gradient high-entropy alloy at high strain rates: Crystal plasticity modeling. Extrem Mech Lett. 70:102194. https://doi.org/10.1016/j.eml.2024.102194

Liu C, Xu W, Liu M, Zhang L (2020) Effect of strain path on roughness evolution of free surface during plastic deformation. Int J Mech Sci. 173. https://doi.org/10.1016/j.ijmecsci.2020.105475

Bong HJ, Lee J (2021) Crystal plasticity finite element-Marciniak-Kuczynski approach with surface roughening effect in predicting formability of ultra-thin ferritic stainless steel sheets. Int J Mech Sci. 191:106066. https://doi.org/10.1016/j.ijmecsci.2020.106066

Liu C, Xu W, Niu T, Chen Y (2022) Roughness evolution of constrained surface based on crystal plasticity finite element model and coupled Eulerian-Lagrangian method. Comput Mater Sci. 201:110900. https://doi.org/10.1016/j.commatsci.2021.110900

Lim Y, Ha S (2023) RufGen: A plug-in for rough surface generation in Abaqus/CAE. SoftwareX. 22:101380. https://doi.org/10.1016/j.softx.2023.101380

Tang X, Wang Z, Wang X, Deng L, Zhang M, Gong P, et al. (2023) Unraveling size-affected plastic heterogeneity and asymmetry during micro-scaled deformation of CP-Ti by non-local crystal plasticity modeling. Int J Plast. 170. https://doi.org/10.1016/j.ijplas.2023.103733

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