CREATIVE PRINT LAYOUT AUTOMATION USING AI
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6646Keywords:
Artificial Intelligence, Generative Design, Layout Optimization, Print Automation, Computational CreativityAbstract [English]
The fast growth of artificial intelligence (AI) has transformed the manner in which design processes function by automating difficult creative jobs such as creating print layouts. This research looks into an AI powered system of making creative print plans automatically to meet the functionality and aesthetics needs. The proposed system uses a combination of several types of Artificial Intelligence, such as Generative Adversarial Networks (GANs), which are used to generate the styles, and Transformers, which are used to understand the meaning of the design content, and Reinforcement Learning (RL), which is used to improve layouts in real time. When these models are assembled, the system produces high quality print layouts on the fly that adhere to set design objectives such as brand unity, readability and visual hierarchy. The program uses a two-part optimisation method which checks for aesthetic and structural consistency. This allows designers to be artistic while still making the system usable. It is suggested that perceived measures be used to judge the aesthetic quality and alignment, contrast and content balance scores be used to judge the functional performance. The solution has a dynamic user interface that enables creators to change results, provide input in real time and the directions of learning. Integration with famous design tools shows how it can be used in real life and how it can improve the speed of printing processes. The conclusion of the study is that AI-based technology can make businesses, schools, and advertising workers much more productive, reduces response times, and will make high quality print design more available to everyone. This work helps to add to a growing field of computer creativity and automatic design.
References
Arriaga-Dávila, J., Rosero-Arias, C., Jonker, D., Córdova-Castro, M., Zscheile, J., Kirchner, R., Aguirre-Soto, A., Boyd, R., De Leon, I., and Gardeniers, H. (2025). From Single to Multi-Material 3D Printing of Glass-Ceramics for Micro-Optics. Small Methods, 9(2), Article 2401809. https://doi.org/10.1002/smtd.202401809 DOI: https://doi.org/10.1002/smtd.202401809
Bankar, A., Sonekar, A. M., Kashyap, A. R., Pathan, A. A., and Ansari, R. S. (2025). Effect of Print Speed, Infill Pattern and Infill Density on Tensile Strength of Part Produced by ABS Filament using FDM 3D Printing Technology. International Journal of Trends in Advanced Research in Mechanical Engineering (IJTARME), 14(1), 41–47.
Chen, H., Liu, Y., Balabani, S., Hirayama, R., and Huang, J. (2023). Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing. Research, 6, Article 197. https://doi.org/10.34133/research.0197 DOI: https://doi.org/10.34133/research.0197
Freeman, S., Calabro, S., Williams, R., Jin, S., and Ye, K. (2022). Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization. Frontiers in Bioengineering and Biotechnology, 10, Article 913579. https://doi.org/10.3389/fbioe.2022.913579 DOI: https://doi.org/10.3389/fbioe.2022.913579
Haque, A. N. M. A., and Naebe, M. (2023). Tensile Properties of Natural Fibre-Reinforced FDM Filaments: A Short Review. Sustainability, 15(24), Article 16580. https://doi.org/10.3390/su152416580 DOI: https://doi.org/10.3390/su152416580
Hassan, M., Misra, M., Taylor, G. W., and Mohanty, A. K. (2024). A Review of AI for Optimization of 3D Printing of Sustainable Polymers and Composites. Composites Part C: Open Access, 15, Article 100513. https://doi.org/10.1016/j.jcomc.2024.100513 DOI: https://doi.org/10.1016/j.jcomc.2024.100513
Johnson, J. E., Jamil, I. R., Pan, L., Lin, G., and Xu, X. (2025). Bayesian Optimization with Gaussian-Process-Based Active Machine Learning for Improvement of Geometric Accuracy in Projection Multi-Photon 3D Printing. Light: Science and Applications, 14, Article 56. https://doi.org/10.1038/s41377-024-01707-8 DOI: https://doi.org/10.1038/s41377-024-01707-8
Kennedy, S. M., Wilson, L. A., and Rb, J. R. (2025). Natural Fiber Filaments Transforming the Future of Sustainable 3D Printing. MethodsX, 14, Article 103385. https://doi.org/10.1016/j.mex.2025.103385 DOI: https://doi.org/10.1016/j.mex.2025.103385
Panico, A., Corvi, A., Collini, L., and Sciancalepore, C. (2025). Multi Objective Optimization of FDM 3D Printing Parameters set via Design of Experiments and Machine Learning Algorithms. Scientific Reports, 15, Article 16753. https://doi.org/10.1038/s41598-025-01016-z DOI: https://doi.org/10.1038/s41598-025-01016-z
Srivastava, M., Aftab, J., and Tyll, L. (2025). The Influence of Artificial Intelligence and Additive Manufacturing on Sustainable Manufacturing Practices and their Effect on Performance. Sustainable Futures, 10, Article 100820. https://doi.org/10.1016/j.sftr.2025.100820 DOI: https://doi.org/10.1016/j.sftr.2025.100820
Wang, G., Chen, Y., An, P., Hong, H., Hu, J., and Huang, T. (2023). UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios. Sensors, 23(16), Article 7190. https://doi.org/10.3390/s23167190 DOI: https://doi.org/10.3390/s23167190
Yampolskiy, M., Bates, P., Seifi, M., and Shamsaei, N. (2022). State of Security Awareness in the Additive Manufacturing Industry: 2020 Survey. Progress in Additive Manufacturing, 7, 192–212. https://doi.org/10.1520/STP164420210119 DOI: https://doi.org/10.1520/STP164420210119
Yeshiwas, T. A., Tiruneh, A. B., and Sisay, M. A. (2025). A Review Article on the Assessment of Additive Manufacturing. Journal of Materials Science: Materials in Engineering, 20(1), Article 85. https://doi.org/10.1186/s40712-025-00306-8 DOI: https://doi.org/10.1186/s40712-025-00306-8
Zhou, L., Miller, J., Vezza, J., Mayster, M., Raffay, M., Justice, Q., Al Tamimi, Z., Hansotte, G., Sunkara, L. D., and Bernat, J. (2024). Additive Manufacturing: A Comprehensive Review. Sensors, 24(9), Article 2668. https://doi.org/10.3390/s24092668 DOI: https://doi.org/10.3390/s24092668
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mohan Garg, Prabhjot Kaur, Saurabh Namdev, Mr. John Bennet Johnson, M. Hari Prasad , Dr. Kunal Meher
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
