Print parameters, solved
A hybrid AI system for FDM 3D printing. Claude reads renders of your model with forced tool-use, so its geometry analysis comes back as a schema, never as prose to be parsed. A physics-constrained gradient-boosting model — trained on profilometer readings from 108 real printed samples — predicts surface roughness. A multi-objective Bayesian optimizer searches the parameter space against it, and PrusaSlicer validates the winners into print-ready G-code.
2025 — 2026 · ROLE: SOLE DEVELOPER · LIVE IN PRODUCTION · FASTAPI · REACT · THREE.JS · SCIKIT-OPTIMIZE
0.99
R² — ROUGHNESS MODEL
0.51 µm
RMSE
108
PROFILOMETER SAMPLES
30+
PRINTER PROFILES
FORCED TOOL-USE FOR STRUCTURED OUTPUT
- Claude analyses multi-angle renders of the STL and must answer through a tool schema
- Overhangs, layer-defect risk and a parameter range come back typed, not as free text
- No prose parsing, so a wording change upstream cannot break the pipeline
MULTI-OBJECTIVE BAYESIAN OPTIMIZATION
- scikit-optimize gp_minimize plus a qNEHVI engine, Pareto post-filtering
- Surface quality traded against print time — the operator picks a point on the front
- Cheap ML screening first, expensive slicer validation only on the finalists
PHYSICS-CONSTRAINED ML
- Gradient boosting with monotonic constraints — layer height cannot improve roughness
- Trained on real lab measurements, not simulated data
- A geometry fingerprint retrieves similar past prints for a warm start
A MEASURABLE FEEDBACK LOOP
- Every recommendation can be answered with "did it actually print well?"
- That ground truth feeds the failure classifier and conformal recalibration
- Every billable model call is logged with tokens and latency, so cost can be recomputed when prices move
AI / VISION
Claude (Anthropic) · forced tool-use · multi-angle STL renders
ML / OPTIMIZATION
scikit-learn · scikit-optimize · qNEHVI · conformal intervals
BACKEND
FastAPI · Python 3.11 · PostgreSQL · SQLAlchemy 2 · Alembic
FRONTEND
React 18 · TypeScript · Three.js · React Three Fiber · Zustand
SLICER: PRUSASLICER CLI · DEPLOY: DOCKER ON AWS EC2 · GRADUATION PROJECT, FSMVÜ