ML-Driven Generative Design Pipeline
Combine Fusion 360 generative design with ML to automate optimal design selection
Last reviewed: March 2026Overview
Fusion 360's generative design engine can produce dozens of structurally valid design variants for a given set of loads, constraints, and manufacturing processes. But selecting the best design from 30+ variants manually — reviewing each one's mass, stiffness, manufacturability, and visual quality — takes hours and relies on subjective judgment. ML-based design scoring can automate this selection, applying consistent, quantifiable criteria across all variants simultaneously.
In this project, you'll build a pipeline that: uses the Fusion 360 API to programmatically launch generative design studies, exports STL/STEP geometry and JSON results for all variants, runs automated FEA evaluation on each variant using Python-scripted Fusion 360 simulations, extracts geometric and performance features, and trains an ML scoring model that predicts overall design desirability from these features.
Beyond single-study automation, you'll implement multi-study active learning: the ML scoring model guides which regions of the generative design space to explore next, progressively focusing computational resources on the most promising design directions. This is how next-generation AI-assisted design tools at Autodesk, Siemens, and Boeing Research work.
What You'll Learn
- ✓ Drive Fusion 360 generative design studies programmatically via the Fusion 360 API
- ✓ Extract and parse geometric and structural performance metrics from generative design results
- ✓ Build an ML scoring model that predicts design desirability from multi-objective performance metrics
- ✓ Implement Pareto front analysis for multi-objective design selection (mass vs. stiffness vs. manufacturability)
- ✓ Design an active learning loop that focuses generative design exploration on high-potential design regions
Step-by-Step Guide
Set Up the Fusion 360 API
Install Fusion 360 (education license available) and explore the Python API via the built-in Script editor. Key modules: adsk.core (application), adsk.fusion (design), adsk.cam (manufacturing). Write a test script that creates a simple parametric part, applies a load, and runs a simulation study.
Study the Generative Design API documentation specifically — it allows programmatic creation of preserve geometries, obstacle geometries, load cases, and manufacturing method constraints. Understand how to set these via script before attempting a full generative study.
Define a Reference Design Problem
Choose a representative aerospace bracket problem: a wing-to-fuselage attach fitting that must carry 10 kN tension load while fitting within a defined envelope, manufactured by aluminum CNC milling. Define the preserve geometry (interface surfaces that cannot be removed), obstacle geometry (keep-out regions for adjacent components), and load cases.
Run a baseline generative design study manually in Fusion 360 to familiarize yourself with the workflow and verify your problem setup produces a reasonable range of designs (typically 20–40 variants). Document the design parameters and result metrics for each variant.
Automate the Export Pipeline
Write a Fusion 360 API script that iterates over all generative design outcomes, extracts: mass (kg), max von Mises stress (MPa), max deflection (mm), surface area (proxy for manufacturing time), and bounding box dimensions. Export each variant as an STL file for geometric ML features.
Build a Python postprocessor that reads the exported JSON result files and STL geometry, computes additional geometric features (mesh volume, surface roughness, aspect ratio of principal bounding box, estimated CNC setup time), and assembles everything into a pandas DataFrame for ML training.
Build the Multi-Objective Scoring Model
Define a composite desirability score weighting mass (minimize), stress margin (maximize), and estimated manufacturing cost (minimize). Run 5 generative studies with varied load cases and constraints to collect 100–200 labeled design variants.
Train a gradient boosted regressor (XGBoost) to predict the composite score from the extracted geometric and structural features. Use leave-one-study-out cross-validation to check generalization across different design problems. SHAP values reveal which features drive the scoring most — typically mass and stress margin dominate, but geometric complexity (manufacturing cost proxy) matters in practice.
Pareto Front Analysis
Implement multi-objective Pareto front extraction: identify the subset of designs for which no other design is simultaneously lighter, stiffer, and cheaper to manufacture. Use PyGMO or pymoo for Pareto front computation. Visualize the Pareto front in 2D projections (mass vs. stress margin, mass vs. manufacturing cost).
Compare the automated Pareto front selection against: (1) single-objective optimization (just minimize mass), (2) expert manual selection from a review of all 40 variants. Quantify how much better the Pareto-aware automated selection is in terms of objective trade-offs captured.
Active Learning Loop
Implement an active learning loop: after training the initial scoring model on 5 studies, use it to predict which generative design constraints (load case direction, preserve geometry size, obstacle geometry) are likely to produce high-scoring variants. Run the next generative study with those constraints, add results to the training set, and retrain.
After 3–4 active learning iterations, compare the best designs found against random exploration (same number of total studies, varied randomly). Quantify the improvement: active learning should consistently find better Pareto fronts with the same computational budget.
Career Connection
See how this project connects to real aerospace careers.
Aerospace Engineer →
Generative design and AI-assisted design selection are being adopted at Airbus, Boeing, GE Aviation, and Lockheed Martin — engineers who can build and manage these pipelines are highly sought
Aerospace Manufacturing →
Manufacturing engineers who can combine generative design with manufacturing process constraints (CNC, AM) and automated evaluation are enabling the next generation of lightweight aerospace structures
Space Operations →
CubeSat and small satellite structural component design is increasingly using generative design to minimize mass under strict volume and structural constraints
Drone & UAV Ops →
UAV frame and component design is a natural application for generative design + ML scoring — minimal mass with sufficient stiffness is exactly the trade-off that matters
Go Further
Extend toward production-grade AI-assisted design:
- Graph neural networks — replace hand-engineered geometric features with a GNN that operates directly on the STL mesh, learning its own geometric representation for scoring
- Manufacturing simulation integration — add a CNC simulation step (using Fusion 360 CAM API) to generate accurate machining time estimates as a real manufacturing cost proxy
- Cross-software pipeline — export optimal generative designs to ANSYS or Abaqus for higher-fidelity FEA validation than Fusion 360's built-in simulator provides
- Generalize to other tools — adapt the ML scoring and active learning approach to SolidWorks Topology Optimization or nTopology for a tool-agnostic design exploration framework