Bridging Additive Manufacturing and Electronics Printing in the Age of AI
Abstract
:1. Introduction
1.1. Printing Electronics
1.2. Additive Manufacturing of Electronics
1.3. AI Automation
1.4. Outline of This Review
2. Printing Electronics
2.1. Research Trends
2.2. Contact Printing
2.3. Non-Contact Printing: Main Techniques
2.4. Non-Contact Printing: Electrostatic Printing and Electrospinning
2.5. Opportunities and Challenges
3. Three-Dimensional Printing of Electronics
3.1. Developments in 3D Printed Electronics
3.2. Supercapacitors by 3D Printing
3.3. Sensors by 3D Printing
3.4. Batteries by 3D Printing
3.5. Fuel Cells by 3D Printing
4. AI in 3D and Electronics Printing
4.1. The Rise of AI
4.2. AI Applications
- (1)
- Inkjet path optimization
- (2)
- Defect prediction
- (3)
- Parameter reverse inference
4.3. Champion Models
4.4. Optimizing Property and Process Simultaneously
4.5. Levels of Automation
4.6. Possibilities
5. The Future of AI-Aided Electronics Printing
5.1. Recent Examples: Soft Robotics and Wearable Electronics
5.2. High Throughput Experimentations
5.3. Environmental Considerations
5.4. Conclusions
- Traditional techniques of electronics printing provide contact and non-contact pattern transferring to fabricate flexible electronics with low-cost, low-temperature processes.
- Additive manufacturing of electronics offers opportunities for multi-material, multi-process, personalized fabrication of sensors, electronics, energy storage, and harvest devices, which are not easily accomplishable by traditional ways of electronics printing.
- AI and algorithm-based optimization vastly improve the efficiency of 3D printing and electronics printing via a high-quality database, digital twin building, and process optimization.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Method | Accuracy (mm) | Material Compatibility | Surface Finish | Print Speed | Strength | Limitation | Equipment Cost |
---|---|---|---|---|---|---|---|
FDM | ±0.05 –0.3 | Thermoplastic | Moderate | 0.04–0.15 m/s | Low cost, Robust | Layer lines | $200– $15,000 |
SLA | ±0.025 –0.1 | Photopolymer | Excellent | 10–100 mm/h | High accuracy | Limited materials | $2500– $25,000 |
DLP | ±0.025 –0.1 | Photopolymer | Excellent | 20–100 mm/h | Faster than SLA | Limited materials | $5000– $50,000 |
SLS | ±0.05 –0.2 | Thermoplastics & composites | Rough | 10–50 mm3/s | Mechanical properties | Powder hazard | $30,000– $200,000 |
Poly Jet | ±0.01 –0.085 | Photopolymer | Excellent | 0.1–0.2 m/s | Very High Resolution | Expensive | $50,000– $500,000 |
DIW | ±0.1 –0.5 | Exceptionally wide | Moderate | 0.001–0.5 m/s | Print multi- materials | Viscosity requirement | $10,000– $100,000 |
Method | Accuracy (%) | Interpret -Ability | Training Need | Use Cases | Adaptability to New Uses | Computational Cost/Complexity |
---|---|---|---|---|---|---|
SVM | 80–85 | High (SHAP) | Low– Moderate | Defect classification, Parameter prediction, Optimization | Moderate | Low to Moderate |
ANN | 80–97 | Low | High | Mechanical property prediction, thermal modeling | Limited | Moderate to High |
CNN | 92–98 | Low– Moderate | High | Layer inspection, defect detection from images | Limited | High |
GAN | 90–99 | Low | Very High | Generation of synthetic data, design | Limited | Very High |
Random Forest | 80–90 | Moderate (feature importance) | Moderate | Materials Screening | High | Moderate |
BO | N/A (Optimi- zation) | Moderate (Visualizable) | Low | Multi-parameter tuning | High | Moderate |
Pareto Front | Optimi- zation | Low | Moderate | Multi-parameter trade-off | Moderate | High |
Improvements by AI | Materials/Process | Model | Ref |
---|---|---|---|
Enhanced interfaces and sensor design | 3D printed particle-matrix composite | Computer vision, finite element analysis | [135] |
Enhanced reliability | 3D printed neuromorphic circuits | Gradient-based optimization | [158] |
Jetting prediction for new inks | Inkjet printed electronics | Decision trees, random forest, gradient boosting, and neural networks | [156] |
Estimating electromagnetic characteristics | Spiral antennas | Gaussian regression | [157] |
Property estimation | Printed circuit board (PCB) | Computer vision for automatic optical inspection (AOI) | [159] |
Resistivity modeling of printed lines | 3D aerosol-jet printing | Convolutional neural networks | [164] |
Predicting resistance | PCB | Computer vision | [165] |
Relating print parameters to physical and electrical properties | Inkjet-printed electronics | Nonlinear regression, k-nearest neighbor (KNN), Gaussian process regression | [166] |
Ink selection system | Screen-printed electronics | MLP | [167] |
Identification of optical quality and functionality | Inkjet-printed electronics | Computer Vision | [163] |
Feature classification | Evaporation-drivenmulti-scale 3D printing | SVM | [168] |
Classification of electrical conductivity based on voltage, nozzle speed, and flow rate | Electro-hydrodynamic-jet of organic electronics | Decision tree classifier, KNN, random forest | [169] |
Strain and humidity sensorsfor health monitoring | Graphene-carbon nanotube ink for jet printing | SVM | [154] |
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Chen, J.; Yuan, Y.; Wang, Q.; Wang, H.; Advincula, R.C. Bridging Additive Manufacturing and Electronics Printing in the Age of AI. Nanomaterials 2025, 15, 843. https://doi.org/10.3390/nano15110843
Chen J, Yuan Y, Wang Q, Wang H, Advincula RC. Bridging Additive Manufacturing and Electronics Printing in the Age of AI. Nanomaterials. 2025; 15(11):843. https://doi.org/10.3390/nano15110843
Chicago/Turabian StyleChen, Jihua, Yue Yuan, Qianshu Wang, Hanyu Wang, and Rigoberto C. Advincula. 2025. "Bridging Additive Manufacturing and Electronics Printing in the Age of AI" Nanomaterials 15, no. 11: 843. https://doi.org/10.3390/nano15110843
APA StyleChen, J., Yuan, Y., Wang, Q., Wang, H., & Advincula, R. C. (2025). Bridging Additive Manufacturing and Electronics Printing in the Age of AI. Nanomaterials, 15(11), 843. https://doi.org/10.3390/nano15110843