3D PRINTING AND AI-GUIDED SCULPTURE FABRICATION

Authors

  • Prerak Sudan Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • B Reddy Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India
  • Anand S. Relkar Department of Mechanical Engineering, Matoshri Aasarabai Institute of Technology & Research Centre, Nashik, Maharashtra, India.
  • Vaibhaw R Doifode Department of Electrical Engineering, Yeshwantrao Chavan College of Engineering,Nagpur, Maharashtra, India
  • Surendra Babu K Associate Professor, Department of Mechanical Engineering,Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation DU, Tamil Nadu, India
  • Dr. Vikas Sagar Assistant Professor, Department of Computer Science & Engineering AI, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6629

Keywords:

3D Printing, Artificial Intelligence, Generative Art, Digital Aesthetics, Computational Creativity, Sculpture Fabrication

Abstract [English]

This study looks at how 3D printing and artificial intelligence (AI) are coming together in the field of sculpture manufacturing, focussing on how they are changing the way art is made, how quickly it can be made, and how it looks in general. The research examines how AI-powered design models and computer creativity can aid people in thinking of new ideas by creating complex sculptures with human assistance. In this research the relationship between human intention and computer activity is being reconceptualized in contemporary art practice within a framework of digital aesthetics, machine learning and material science. The theoretical background focuses on how additive manufacturing has evolved in the creative areas, the use of AI-assisted generative design, and the social issues that arise when machine-generated art is produced. The project takes an experimental approach to the generation of led AI models by using a mix of neural networks, computer-aided design (CAD) systems and 3D printer tools to create and test the models. The suggested system design consists of training models, integrating process between artificial intelligence and printers and optimising materials for accurate manufacturing. Comparing sculptures that were made by humans and those that were made by AI can help us understand how to make better statues and come up with new designs. The sample building results show that it is possible to build complex structures that weren't possible before, thanks to the AI-guided 3D printing technology, which also reduces waste and the need for human assistance.

References

Ajani, S., and Wanjari, M. (2013). An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering. In Proceedings of the 5th International Conference on Computational Intelligence and Communication Networks (CICN 2013). https://doi.org/10.1109/CICN.2013.106 DOI: https://doi.org/10.1109/CICN.2013.106

Bende, M., Khandelwal, M., Borgaonkar, D., and Khobragade, P. (2023). VISMA: A Machine Learning Approach to Image Manipulation. In 2023 6th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1–5). IEEE. https://doi.org/10.1109/ISCON57294.2023.10112168 DOI: https://doi.org/10.1109/ISCON57294.2023.10112168

Boretti, A. (2024). A Techno-Economic Perspective on 3D Printing for Aerospace Propulsion. Journal of Manufacturing Processes, 109, 607–614. https://doi.org/10.1016/j.jmapro.2023.12.044 DOI: https://doi.org/10.1016/j.jmapro.2023.12.044

Freeman, S., Calabro, S., Williams, R., Jin, S., Ye, K. (2022). Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization. Frontiers in Bioengineering and Biotechnology, 10, 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, 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, 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, 56. https://doi.org/10.1038/s41377-024-01707-8 DOI: https://doi.org/10.1038/s41377-024-01707-8

Metal AM. (2023). The Convergence of Additive Manufacturing and Artificial Intelligence: Envisioning a Future that is Closer than you Think. Metal AM.

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, 16753. https://doi.org/10.1038/s41598-025-01016-z DOI: https://doi.org/10.1038/s41598-025-01016-z

Sheela, M. A. A., Tejaswi, M., Prakash, N. B., Tulasi, M. D. S., and Anitha, K. (2025). IntegriScan: A Graph-Aided Model for Detecting Corrupted and Anomalous Data Patterns. IJRAET, 14(1), 71–78.

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, 100820. https://doi.org/10.1016/j.sftr.2025.100820 DOI: https://doi.org/10.1016/j.sftr.2025.100820

Thomas, D. J. (2022). Advanced Active-Gas 3D Printing of 436 Stainless Steel for Future Rocket Engine Structure Manufacture. Journal of Manufacturing Processes, 74, 256–265. https://doi.org/10.1016/j.jmapro.2021.12.037 DOI: https://doi.org/10.1016/j.jmapro.2021.12.037

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, 7190. https://doi.org/10.3390/s23167190 DOI: https://doi.org/10.3390/s23167190

Wang, W., Wang, P., Zhang, H., Chen, X., Wang, G., Lu, Y., Chen, M., Liu, H., and Li, J. (2023). A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies. Micromachines, 15, 28. https://doi.org/10.3390/mi15010028 DOI: https://doi.org/10.3390/mi15010028

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, 2021, 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, 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, 2668. https://doi.org/10.3390/s24092668 DOI: https://doi.org/10.3390/s24092668

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Published

2025-12-10

How to Cite

Sudan, P., B Reddy, Relkar, A. S., Doifode, V. R., Babu K, S., & Sagar, V. (2025). 3D PRINTING AND AI-GUIDED SCULPTURE FABRICATION. ShodhKosh: Journal of Visual and Performing Arts, 6(1s), 478–487. https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6629