If you are using this tool in your work, please cite the following paper:
Zhou, S., Ben Bettaieb, M. & Abed-Meraim, F. Flexible Generation of Polycrystalline Representative Volume Elements on Pre-Meshed Geometries: A Novel ABAQUS Toolbox for Crystal Plasticity Simulations. Integr Mater Manuf Innov 15, 616–637 (2026). https://doi.org/10.1007/s40192-026-00466-1
28-07-2026： add lines to remove the random/user-defined seeds added to the nodes in .inp file.
01-08-2026：
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add seed points output for the general module (seed_points.csv in your work directory)
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add the uniform grains module (see below)
08-08-2026：add Periodic Grains module
14-08-2026： add one element one grain module
15-08-2026: add cube/square grain module
Usage instruction: input parameters and then click the OK/Apply button. There is no need to create the geometry and mesh manually.
upcoming: grain boundary
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.
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
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.
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)
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.
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.
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.
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