Environmental Impact & Sustainability
Machine learning and Industry 4.0 technologies enable comprehensive energy monitoring, life cycle assessment, and waste reduction strategies for environmentally sustainable additive manufacturing.
Hassan et al., Composites Part C (2024) - Section 10: Environmental Impact
Key Impact Metrics
98.2%
LSTM accuracy classifying energy stages (print/standby/preheat)
30.9×
Waste reduction from ANN-optimized exoskeleton design
Real-time
Live LCA with cyber-physical production systems
Highlighted Studies
| Study | Highlights |
|---|---|
|
LSTM Energy Classification
Time-Series Deep Learning
|
Achieved 98.2% accuracy classifying energy consumption stages (printing, standby, preheating) across PLA, ABS, and PETG materials. |
|
ANN-Optimized Design
Circular Economy Approach
|
Reduced waste 30.9× through optimized exoskeleton design, enabling one free print for every 6.67 prints via material recycling. |
|
Live LCA Systems
Cyber-Physical Integration
|
Real-time environmental impact monitoring using Taguchi L-9 orthogonal arrays integrated with cyber-physical production systems. |
Sustainable AM requires holistic optimization across material selection, process parameters, and end-of-life considerations.