During the MODISIM MANIA 2.0 competition, I had my first experience exploring parametric and optimization design through the Tablet Design Challenge. That event was a turning point in how I understood digital design — it taught me the value of parameterization, optimization, and simulation-driven engineering. The knowledge I gained there became fundamental for my current robotic design project, especially when working with the parametric study tools in 3DEXPERIENCE SIMULIA.

For this stage of the project, I focused on all of the mechanical parts of my 6DOF robot, where the goal was to select the optimal geometry that would minimize material usage while maintaining mechanical strength.

⚙️ How to Perform a Parametric Study in SIMULIA 3DEXPERIENCE⚙️

One of the most powerful ways to optimize a design in 3DEXPERIENCE is by performing a parametric study.
This process automatically analyzes several variations of a model by changing key parameters, allowing engineers to find the most efficient configuration without running individual simulations manually.

Below is the workflow I used for my Elbow 2 parametric optimization:

 

🔹 1. Prepare parameters in CATIA (3D Part Design)🔎​

Before starting the study, it’s essential to define your design parameters in CATIA Part Design.
These may include dimensions such as thickness, radii, lengths, or angles, which will later be used as design variables.
You also need to have an initial simulation configured in SIMULIA, which will serve as your reference setup.

🔹 2. Open the Parametric Design Study app​

Once your model and initial simulation are ready, open the Parametric Design Study app in SIMULIA.
From there, create a new Design Improvement Study, which lets you evaluate how your defined parameters influence your model’s performance.
 

🔹 3. Define design variables​❗

In the Design Variables Manager, add the parameters you created in CATIA.
For each variable, define a Step Size, which is the increment used in the analysis.
In my case, I set a 1 mm step size for each iteration to evaluate small, precise dimensional changes in the elbow.
 

🔹 4. Set response variables

In the Response Variables Manager, select the performance objectives that you want to track throughout the study.
Based on my initial simulation, I selected:

• Mass,
• Von Mises stress,
• Total displacement.
• 

These outputs helped me identify the best trade-off between lightweight design, strength, and stiffness.

 

🔹 5. Verify the design space

Before running the study, it’s important to use the Design Space Check tool.
This feature validates whether each design point can generate a valid model, mesh, and scenario for the analysis.
I configured 15 design space checks, which provided enough data points to detect trends without overloading the computation.
 

🔹 6. Configure the study and run the simulation

In the Study Configuration section, I set the Sample Mode to Guided and adjusted the image resolution to Low to speed up computation time.
Once everything was configured, I launched the simulation — the system automatically ran each iteration by varying the defined parameters.

🔹 7. Analyze results and select the best design

After the analysis finished, the results displayed all iterations, showing which design met the objectives most effectively.
I selected my favorite designs, which offered lower mass and displacement without exceeding the PLA stress limits.
Finally, these optimized configurations were saved in my 3D Space, becoming part of my overall robot design project.
 

🔹 Conclusion

The Parametric Design Study in SIMULIA is a powerful automation tool that optimizes multiple designs simultaneously, saving both time and computational effort.
Instead of running each simulation one by one, it allows engineers to visualize how their models behave under different conditions, making it easier to take data-driven engineering decisions within the 3DEXPERIENCE ecosystem.

✅ Final Result✅

The final outcome was a functional, visually clean, and 3D-print-optimized elbow that demonstrates how a digital workflow — from parametric modeling in CATIA to advanced simulation in SIMULIA — can enhance both performance and sustainability in design.
This experience helped me strengthen my skills in digital design optimization and explore how simulation-driven engineering leads to better robotics solutions.