GENERATIVE DESIGN IN 3D PRINTING EDUCATION

Authors

  • Tavishi Limaye Assistant Professor, Department of Development Studies, Vivekananda Global University, Jaipur, India
  • Durga Prasad Associate,Professor,School,of,Engineering,&,Technology,,Noida,international,University,203201
  • Sourav Rampal Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India
  • Kumaran P Assistant Professor, Department of Mechanical Engineering,Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation(DU), Tamil Nadu, India
  • Ms. Vyshnavi A Assistant Professor, Department of Management Studies, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India,
  • Jaspreet Sidhu Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Pooja Abhijeet Alone Department of Engineering, Science and Humanities Vishwakarma Institute of Technology, Pune, Maharashtra, 411037 India.

DOI:

https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6765

Keywords:

Generative Design, 3D Printing Pedagogy, Computational Creativity, Experiential Education, Digital Fabrication, Reflective Practice, AI-Integrated Curriculum, Educational Innovation

Abstract [English]

The paper examines how generative design and 3D printing can be incorporated in the contemporary design education and how it will enhance the computational creativity as well as reflective practice and interdisciplinary learning. This study will apply the design-based mixed-method approach to analysis, based on the constructivist, constructionist, and experiential theories of learning, to address whether the use of algorithmic modeling and additive manufacturing can have an impact on the student learning outcomes. An implementation framework was developed and executed based on project based modules in which exploration, parametrical modeling and concrete prototyping were highlighted through iterations. Quantitative data revealed that the areas of the computational literacy (↑44.8), creativity (↑48.3) and reflective learning (↑65.4) changed significantly, but the qualitative data indicated that the engagement and cognitive flexibility increased. Findings indicate that generative design brings about a paradigm shift in the sense of following the tools acquisition to co-create ideas, and technology is considered a proactive partner in the design thinking. The article also singles out the issues of algorithmic authorship, data ethics, and accessibility and concludes that adaptive AI-based and policy-affirmative models of the sustainable implementation of the curriculum are required. This work of writing contributes to a replicable model of pedagogy which is a combination of calculation, creativity and material experimentation; a redefinition of future of design education.

References

Aljabali, B. A., Shelton, J., and Desai, S. (2024). Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0. Materials, 17, 4544. https://doi.org/10.3390/ma17184544 DOI: https://doi.org/10.3390/ma17184544

Aman, B. (2020). Generative Design for Performance Enhancement, Weight Reduction, and its industrial Implications [arXiv preprint]. arXiv.

Chang, C., Yang, Y., Pei, L., Han, Z., Xiao, X., and Ji, Y. (2022). Heat Transfer Performance of 3D-printed Aluminium Flat-Plate Oscillating Heat Pipes for the Thermal Management of LEDs. Micromachines, 13, 1949. https://doi.org/10.3390/mi13111949 DOI: https://doi.org/10.3390/mi13111949

Chen, M. C., Zhao, Y., and Xie, Y. M. (2019). Topology Optimization and Additive Manufacturing of Nodes in Spatial Structures. China Civil Engineering Journal, 52, 1–10.

Khan, S., and Awan, M. J. (2018). A Generative Design Technique for Exploring Shape Variations. Advanced Engineering Informatics, 38, 712–724. https://doi.org/10.1016/j.aei.2018.09.005 DOI: https://doi.org/10.1016/j.aei.2018.10.005

Lu, S., Ma, D., and Mi, X. (2024). A High-Throughput Circular Tumor Cell Sorting Chip with Trapezoidal Cross Section. Sensors, 24, 3552. https://doi.org/10.3390/s24113552 DOI: https://doi.org/10.3390/s24113552

Mirzaei, H., Ramezankhani, M., Earl, E., Tasnim, N., Milani, A. S., and Hoorfar, M. (2022). Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors. Sensors, 22, 7696. https://doi.org/10.3390/s22207696 DOI: https://doi.org/10.3390/s22207696

Muthumanickam, N. K., Duarte, J. P., Nazarian, S., Memari, A., and Bilén, S. G. (2021). Combining AI and BIM in the Design and Construction of a Mars Habitat. In The Routledge Companion to Artificial Intelligence in Architecture (pp. 251–279). Routledge. DOI: https://doi.org/10.4324/9780367824259-17

Plocher, J., and Panesar, A. (2019). Review on Design and Structural Optimisation in Additive Manufacturing: Towards next-Generation Lightweight Structures. Materials and Design, 183, 108164. https://doi.org/10.1016/j.matdes.2019.108164 DOI: https://doi.org/10.1016/j.matdes.2019.108164

Snijder, A. H., Linden, L., Goulas, C., Louter, C., and Nijsse, R. (2020). The Glass Swing: A Vector Active Structure Made of Glass Struts and 3D-Printed Steel Nodes. Glass Structures and Engineering, 5, 99–116. Https://Doi.Org/10.1007/S40940-020-00119-8 DOI: https://doi.org/10.1007/s40940-019-00110-9

Vergara Vidal, J. E., Álvarez Campos, D., Dintrans Bauer, D., and Asenjo Muñoz, D. (2021). CORVI, Tipologías De Viviendas Racionalizadas: un ejercicio de estandarizacion. Arquitecturas del sur, 39, 118–137. DOI: https://doi.org/10.22320/07196466.2021.39.059.07

Wang, L. X., Du, W. F., Zhang, F., Zhang, H., Gao, B. Q., and Dong, S. L. (2021). Research on Topology Optimization Design and 3D Printing Manufacturing of Cast Steel Joints with Branches. Journal of Building Structures, 42, 37–49. (In Chinese).

Yu, W., Sing, S. L., Chua, C. K., and Tian, X. (2019). Influence of Re-Melting on Surface Roughness and Porosity of AlSi10Mg Parts Fabricated by Selective Laser Melting. Journal of Alloys and Compounds, 792, 574–581. https://doi.org/10.1016/j.jallcom.2019.04.072 DOI: https://doi.org/10.1016/j.jallcom.2019.04.017

Zhao, Y., Chen, M. C., and Wang, Z. (2019). Additive Manufacturing Oriented Topology Optimization of Nodes in Cable-Strut Structures. Journal of Building Structures, 40, 58–68.

Downloads

Published

2025-12-20

How to Cite

Limaye, T., Prasad, D. ., Rampal, S., P, K., A, V. ., Sidhu, J., & Alone, P. A. (2025). GENERATIVE DESIGN IN 3D PRINTING EDUCATION. ShodhKosh: Journal of Visual and Performing Arts, 6(3s), 469–478. https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6765