Functional Transformation of Artificial Intelligence-Supported 3D Printing Technology in the Field of Sculpture in the 21st Century
DOI:
https://doi.org/10.64293/mentor.v1i2.15Keywords:
Artificial intelligence, 3D printing, Sculpture, Functionality, Sougwen ChungAbstract
In this study, the functional contributions of artificial intelligence-supported 3D printing technology—one of the innovative technologies of the 21st century—are discussed within the context of sculpture art from an academic perspective. Historically, sculpture has been performed with traditional production methods for a long time as a form of expression reflecting the aesthetic, cultural and technical accumulations of humanity. However, with the integration of digitalization and artificial intelligence applications into artistic production processes, it is observed that a new era has begun in sculpture art. The aim of the study is to examine the functionality of artificial intelligence-supported 3D printing technology and is limited to Sougwen Chung's Life Lines study. In this process, descriptive analysis and logical reasoning approaches from qualitative research methods were adopted. The findings revealed that artificial intelligence-supported 3D printing technology offers an innovative approach in the field of sculpture and the impact of these innovations on production processes through the Life Lines study. The results showed that artificial intelligence-supported 3D printing technology has transformative potential in sculpture art.
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Zheng, X. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
Abramson, H. (2018). The digital sculpture toolbox: Eight technologies with significant impact on the future of sculpture. Sculpture Review, 67(2), 34-39.
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160.
Ahmed, H. T., & Aly, A. M. (2023). Recycled waste materials in landscape design for sustainable development (Al-Ahsa as a model). Sustainability, 15(15), 11705.
Aimar, A., Palermo, A., & Innocenti, B. (2019). The role of 3D printing in medical applications: a state of the art. Journal of healthcare engineering, 2019(1), 5340616.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82-115.
Azad, M. A., Olawuni, D., Kimbell, G., Badruddoza, A. Z. M., Hossain, M. S., & Sultana, T. (2020). Polymers for extrusion-based 3D printing of pharmaceuticals: A holistic materials–process perspective. Pharmaceutics, 12(2), 124.
Bingöl, O. (2017). Alternatif Tasarlamalar Bağlamında Fotoğraf. İletişim Çalışmaları Dergisi, 3(1), 1-16.
Bräuer-Burchardt, C., Munkelt, C., Bleier, M., Heinze, M., Gebhart, I., Kühmstedt, P., & Notni, G. (2023). Underwater 3D scanning system for cultural heritage documentation. Remote Sensing, 15(7), 1864.
Bushak, S. (2023). The Image Of The Cossack Mama In Sculpture Technıques. Humanities Science Current Issues.
Chen, Y. (2022). Advantages of 3D printing for circular economy and its influence on designers. Proceedings of the Design Society, 2, 991-1000.
Cui, L., & Misdih, M. (2023, June). Adaptive Hierarchical Optimization of Sculpture 3D Printing Based on Fuzzy PID Algorithm. In 2023 International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII) (pp. 307-310). IEEE.
Derudas, P., & Berggren, Å. (2021). Expanding field-archaeology education: The integration of 3D technology into archaeological training. Open Archaeology, 7(1), 556-573.
Dobbelaere, D. (2015). 3D Printing and the Implications on Intellectual Property from a Belgian-European Perspective. Master Disertation, Master of Law Facultu of Law Ghent University.
Echavarria, K. R., Janzon, K., & Wright, J. (2018). Participatory co-creation of public sculpture incorporating 3D digital technologies. In The 16th EUROGRAPHICS Workshop on Graphics and Cultural Heritage (EG GCH). Eurographics Association.
Elakkad, A. S. (2019). 3D technology in the automotive industry. International Journal of Engineering and Technical Research, 8(11), 110-122.
Gao, C., Wang, F., Hu, X., & Zhang, M. (2023). Research on the analysis and application of polymer materials in contemporary sculpture art creation. Polymers, 15(12), 2727.
Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and machines, 30(1), 99-120.
Iftekar, S. F., Aabid, A., Amir, A., & Baig, M. (2023). Advancements and limitations in 3D printing materials and technologies: a critical review. Polymers, 15(11), 2519.
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
Jégo, J. F., & Meneghini, M. B. (2020, July). Let's Resonate: How to Elicit Improvisation and Letting Go in Interactive Digital Art. In Proceedings of the 7th International Conference on Movement and Computing (pp. 1-8).
Kantaros, A., Ganetsos, T., & Petrescu, F. I. T. (2023). Three-dimensional printing and 3D scanning: emerging technologies exhibiting high potential in the field of cultural heritage. Applied Sciences, 13(8), 4777.
Kessler, A., Hickel, R., & Reymus, M. (2020). 3D printing in dentistry—State of the art. Operative dentistry, 45(1), 30-40.
Khunti, R. (2018). The problem with printing Palmyra: exploring the ethics of using 3D printing technology to reconstruct heritage. Studies in digital heritage, 2(1), 1-12.
Li, H., Zhang, B., Wang, R., Yang, X., He, X., Ye, H., ... & Ge, Q. (2022). Solvent‐free upcycling vitrimers through digital light processing‐based 3D printing and bond exchange reaction. Advanced functional materials, 32(28), 2111030.
Ligon, S. C., Liska, R., Stampfl, J., Gurr, M., & Mülhaupt, R. (2017). Polymers for 3D printing and customized additive manufacturing. Chemical reviews, 117(15), 10212-10290.
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014, June). The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations (pp. 55-60).
Martyastiadi, Y. S. (2020, June). From Spiritual to Virtual: An Interactive Digital Art Creation of Virtual Reality Borobudur. In 2020 Nicograph International (NicoInt) (pp. 18-25). IEEE.
Menano, L., Fidalgo, P., Santos, I. M., & Thormann, J. (2019). Integration of 3D printing in art education: A multidisciplinary approach. Computers in the Schools, 36(3), 222-236.
Nelson, M. D., Goenner, B. L., & Gale, B. K. (2023). Utilizing ChatGPT to assist CAD design for microfluidic devices. Lab on a Chip, 23(17), 3778-3784.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
Rahim, T. N. A. T., Abdullah, A. M., & Md Akil, H. (2019). Recent developments in fused deposition modeling-based 3D printing of polymers and their composites. Polymer Reviews, 59(4), 589-624.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5), 206-215.
Serrano, D. R., Kara, A., Yuste, I., Luciano, F. C., Ongoren, B., Anaya, B. J., ... & Lalatsa, A. (2023). 3D printing technologies in personalized medicine, nanomedicines, and biopharmaceuticals. Pharmaceutics, 15(2), 313.
Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56.
Tong, Z., & Kulic, D. (2021, April). Learning to engage in interactive digital art. In Proceedings of the 26th International Conference on Intelligent User Interfaces (pp. 275-279).
Vavrik, D., Kumpova, I., Vopalensky, M., & Zemlicka, J. (2019). Mapping of XRF data onto the surface of a tomographically reconstructed historical sculpture. Journal of Instrumentation, 14(02), C02003.
Villegas Broncano, M. Á., & Durán Suárez, J. A. (2021). Historical and technical insight into the human motifs in the glass sculpture.
Yang, Z. (2022). Application and development of digital enhancement of traditional sculpture art. Scientific Programming, 2022(1), 9095577.
Yu, C., Schimelman, J., Wang, P., Miller, K. L., Ma, X., You, S., ... & Chen, S. (2020). Photopolymerizable biomaterials and light-based 3D printing strategies for biomedical applications. Chemical reviews, 120(19), 10695-10743.
Yu, J. (2020, November). The Application of 3D Printing Technology in Sculpture. In International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (pp. 755-759). Cham: Springer International Publishing.
Downloads
Published
Versions
- 2025-07-14 (3)
- 2025-07-14 (2)
- 2025-07-14 (1)
How to Cite
Issue
Section
License
Copyright (c) 2025 Mehmet Akif Özdal
This work is licensed under a Creative Commons Attribution 4.0 International License.
