Generative Design Automation with SolidWorks API
Write a Python script that designs, evaluates, and selects structural brackets autonomously
Last reviewed: March 2026Overview
Modern aerospace structures teams don't just design one bracket — they evaluate hundreds of variants to find the one that minimizes weight while meeting stress, stiffness, and manufacturing constraints. Doing this manually in CAD is impractical. Design automation through the CAD API lets engineers define the design space parametrically and explore it systematically, turning weeks of manual work into an overnight compute run.
In this project, you'll connect Python to SolidWorks through its COM API (win32com) to drive the full design-evaluate-select loop. Your Python script will modify design parameters (bracket thickness, rib configuration, fillet radii, hole pattern), trigger SolidWorks Simulation (FEA) studies, extract von Mises stress and deflection results, log them to a database, and finally select the Pareto-optimal design trading off mass vs. structural performance.
This capability — programmatic CAD + automated FEA + ML-guided search — is the core of generative design workflows at Airbus, GE Aviation, and Lockheed Martin. SolidWorks API experience is a significant differentiator on aerospace engineering resumes, and the Python automation skills transfer directly to other CAD platforms (CATIA, NX, Creo) that expose similar APIs.
What You'll Learn
- ✓ Connect Python to SolidWorks via the COM API using win32com and automate part and assembly operations
- ✓ Define parametric bracket designs with equations-driven dimensions in SolidWorks
- ✓ Programmatically set up and run SolidWorks Simulation FEA studies and extract results
- ✓ Build a design-of-experiments pipeline with Latin Hypercube Sampling over the design space
- ✓ Identify Pareto-optimal designs trading off structural mass against maximum stress
Step-by-Step Guide
Connect Python to SolidWorks
SolidWorks exposes a COM automation interface accessible from Python via win32com.client. Connect with: swApp = win32com.client.Dispatch("SldWorks.Application"). Explore the SolidWorks API documentation (Help → API Help) to understand the object hierarchy: Application → Document → Feature → Sketch → Dimension.
Write a test script that opens an existing part, reads a named dimension value, modifies it, rebuilds the model, and saves. Verify the model updates correctly. This basic connect-modify-rebuild cycle is the foundation of all subsequent automation.
Build a Parametric Bracket Model
Create a bracket part in SolidWorks with key dimensions driven by named global variables: base thickness (t_base), rib height (h_rib), rib thickness (t_rib), fillet radius (r_fillet), and mounting hole diameter (d_hole). Use SolidWorks Equations to link features to these variables.
From Python, modify global variables using the GetEquationMgr() interface. Write a function set_bracket_params(t_base, h_rib, t_rib, r_fillet) that sets all variables and triggers a model rebuild. Verify the geometry updates correctly by checking the mass property.
Automate FEA Setup and Execution
Add a SolidWorks Simulation study to your bracket model. Use the Simulation API to: apply fixed fixtures to the mounting face, apply a 5 kN load to the bracket arm, mesh with a specified element size, run the analysis, and extract maximum von Mises stress and maximum deflection.
Wrap this in a Python function run_fea() -> (max_stress_MPa, max_deflection_mm). Test that it reliably runs without SolidWorks crashing — COM automation can be fragile; add try/except blocks and timeout handling from the start.
Generate the Design Dataset
Define parameter bounds based on manufacturing constraints: t_base (3–10 mm), h_rib (10–50 mm), t_rib (2–8 mm), r_fillet (1–5 mm). Use Latin Hypercube Sampling to generate 200 design points. For each design: set parameters, rebuild, check for geometry errors (intersections, zero-thickness features), run FEA if valid, and log results to a CSV file.
Expect 10–20% of random designs to fail geometry checks. Log these as invalid and continue — they reveal the geometric constraints that are implicit in the CAD model.
Optimize with a Surrogate Model
Train a Gaussian Process surrogate on the 200-point dataset: inputs are the 4 design parameters, outputs are mass and max stress. Use the surrogate to run a fast optimization: minimize mass subject to max_stress < 200 MPa (aluminum yield). The surrogate evaluates in microseconds vs. ~30 seconds for the full CAD+FEA pipeline.
Identify the top 10 candidate designs from the surrogate-predicted Pareto front. Run the full CAD+FEA pipeline on these candidates to get ground-truth performance — the surrogate is used for exploration, but the final selection must be validated with the actual simulation.
Export and Document Optimal Designs
For the 3 best designs (by different objective weightings — lightest, stiffest, and best compromise), export: SolidWorks part files, STEP files for downstream use, FEA result screenshots, and a mass properties report. Generate a design comparison table programmatically from your logged data.
Create a Python-generated PDF report (using reportlab or weasyprint) summarizing the design exploration: parameter ranges explored, Pareto front plot, and the selected design with justification. This kind of automated reporting is a professional-grade capability.
Career Connection
See how this project connects to real aerospace careers.
Aerospace Engineer →
CAD automation and design exploration are increasingly expected skills in structures and mechanical engineering roles at Boeing, Airbus, and tier-1 suppliers
Aerospace Manufacturing →
Manufacturing engineers use CAD API automation to generate part family variants, tooling designs, and fixture layouts — the same Python-COM approach applies across SolidWorks, NX, and Creo
Avionics Technician →
Avionics packaging engineers who can automate bracket and enclosure design generation provide enormous value in high-mix, low-volume production environments
Space Operations →
Spacecraft structural design involves hundreds of bracket and fitting variants; automation skills cut design cycle times from months to weeks on satellite programs
Go Further
Push this project toward production-grade design automation:
- Topology optimization integration — use SolidWorks Simulation's topology optimization feature via API to generate organic, minimum-mass bracket shapes as starting points for parametric refinement
- Manufacturing constraints — add geometric checks for CNC machinability (minimum feature size, no undercuts) and AM buildability (overhang angle limits) as design filters before FEA
- Reinforcement learning — replace Latin Hypercube + surrogate with an RL agent that learns to propose good bracket designs by interacting with the CAD+FEA environment
- CATIA/NX port — adapt the automation scripts for CATIA V5/V6 (using pycatia) or Siemens NX (using NXOpen Python) to make your skills platform-independent