Related Experiment Video
Updated: Apr 11, 2026
10:49
Planar and Three-Dimensional Printing of Conductive Inks
Published on: December 9, 2011
37.7K
Multi-objective optimization of printer control parameters for 3D printing of millet dough
Sanket Balasaheb Kokane1, Vinkel Kumar Arora1, Senthilkumar Thangalakshmi1
1Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management-Kundli, Sonipat, India.
Journal of the Science of Food and Agriculture
|September 11, 2025
Summary
Optimizing three-dimensional (3D) food printing of millet dough involves adjusting nozzle diameter, printing speed, layer height, and infill density. Artificial neural networks (ANN) offer superior prediction accuracy for enhanced printing precision.
Area of Science:
- Food Science and Technology
- Materials Science
- Engineering
Background:
- Three-dimensional (3D) food printing allows for customized food products with complex designs.
- The impact of printer settings on millet dough performance requires further investigation.
Purpose of the Study:
- To explore how printer control parameters affect millet dough printing.
- To enhance printing precision using height ratio, mass flow rate, and bending angle metrics.
Main Methods:
- Investigated nozzle diameter, printing speed, layer height, and infill density.
- Employed Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) for predictive modeling.
- Utilized RSM with desirability function (RSMDF) and ANN with genetic algorithm (ANNGA) for multi-objective optimization.
Main Results:
- Infill density and nozzle diameter significantly influenced height ratio.
- Layer height and nozzle diameter impacted mass flow rate.
- Artificial Neural Networks (ANN) demonstrated higher prediction accuracy (R² values of 0.97-0.99) compared to RSM.
- ANNGA showed marginally better performance in multi-objective optimization than RSMDF.
Conclusions:
- Optimal printing conditions were determined using ANNGA for maximum height ratio and mass flow rate, and minimum bending angle.
- The study provides insights into optimizing 3D food printing parameters for millet-based formulations.
