AI-Driven Process Optimization
Particle swarm optimization, reinforcement learning, and CNN-enabled calibration pipelines are transforming FFF/FDM printing by dramatically reducing cost, time, and scrap rates while improving repeatability.
Hassan et al., Composites Part C (2024) - Section 5: Process Optimization
Key Impact Metrics
87%
Cost reduction achieved by PSO Experimenter (from $24.62 to $3.22)
97%
Parameter tuning time reduction for open-source platforms
Real-time
CNN defect detection with simulated annealing calibration
Highlighted Studies
| Study | Highlights |
|---|---|
|
PSO Experimenter
Particle Swarm Optimization
|
Cut stool build cost from $24.62 to $3.22 (87% reduction) while reducing parameter tuning time by 97% for open-source FDM platforms. |
|
Autonomous Calibration System
CNN + Simulated Annealing
|
Combines cameras with CNN defect detection and simulated annealing to identify clogs, layer shifts, and infill drift in real time. |
|
Reinforcement Learning Agents
Adaptive Parameter Control
|
RL agents iteratively learn optimal nozzle temperatures, raster angles, and speeds, enabling repeatable builds for complex geometries and recycled feedstocks. |
Process optimization is tightly coupled with quality control—many systems use feedback loops to adjust parameters based on detected defects.