Українська
Liu, W., & Kolisnyk, O.
(2024).
Parametric modeling as an innovative approach in graphic design.
Art and Design,
7(1),
34-45.
https://doi.org/10.30857/2617-0272.2024.1.3
English
Article
Parametric modeling as an innovative approach in graphic design
Received 01.12.2023, Revised 30.01.2024, Accepted 03.03.2024
Abstract
Purpose. The article focuses on exploring the possibilities and strategies of innovative integration of parametricism into the field of graphic design and analyzing the main problems and potential solutions in using parametric design in creating graphic objects. Methodology. According to the research topic, the methods employed include comparative study, theoretical analysis, and synthesis. Results. The article discusses the definitions and characteristics of parametric design, examines its applications for optimizing design thinking and creating personalized objects. Various levels of innovative implementations are presented, offering several strategies for integrating parametric modeling into graphic design, including visual element generation, layout optimization, design interactivity enhancement, and data visualization in personalized design. Additionally, integration challenges related to the use of artificial intelligence technologies are discussed. Scientific novelty. The article analyzes various levels and effective strategies for applying parametric modeling in the field of graphic design, exploring current aspects of interaction that are innovative and emphasizing the importance of utilizing parametric design methodology, artificial intelligence, and computational design in the paradigm of contemporary graphic design. Practical significance. The materials presented in the article can be utilized for investigating the innovative application of digital design, parametric design, and artificial intelligence in the field of graphic design
Keywords:
parametric modeling; parametrism; parametric design; graphic design; artificial intelligence; digital design
- 441 Views
- Read article
References
[1] Abdel-Rahman, W.S.M. (2021). Thermal performance optimization of parametric building envelope based on bio-mimetic inspiration. Ain Shams Engineering Journal, 12(1), 1133-1142. doi: 10.1016/j.asej.2020.07.007.
[2] Alcaide-Marzal, J., Diego-Mas, J.A., & Acosta-Zazueta, G. (2020). A 3D shape generative method for aesthetic product design. Design Studies, 66, 144-176. doi: 10.1016/j.destud.2019.11.003.
[3] Caetano, I., Santos, L., & Leitão, A. (2020). Computational design in architecture: Defining parametric, generative, and algorithmic design. Frontiers of Architectural Research, 9(2), 287-300. doi: 10.1016/j.foar.2019.12.008.
[4] Danhaive, R., & Mueller, C.T. (2021). Design subspace learning: Structural design space exploration using performance-conditioned generative modeling. Automation in Construction, 127, article number 103664. doi: 10.1016/j.autcon.2021.103664.
[5] ElBatran, R.M., & Ismaeel, W.S. (2021). Applying a parametric design approach for optimizing daylighting and visual comfort in office buildings. Ain Shams Engineering Journal, 12(3), 3275-3284. doi: 10.1016/j.asej.2021.02.014.
[6] Eltaweel, A., & Su, Y. (2017). Parametric design and daylighting: A literature review. Renewable and Sustainable Energy Reviews, 73, 1086-1103. doi: 10.1016/j.rser.2017.02.011.
[7] Gill, S.S., et al. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, article number 100514. doi: 10.1016/j.iot.2022.100514.
[8] Grosman, S., Macorini, L., & Izzuddin, B.A. (2022). Parametric nonlinear modelling of 3D masonry arch bridges. In B.H.V. Topping & J. Kruis (Eds.), Proceedings of the fourteenth international conference on computational structures technology. Edinburgh: Civil-Comp Press. doi: 10.4203/ccc.3.9.3.
[9] Kandikjan, T., Djokikj, J., Mircheski, I., & Angeleska, E. (2022). Integrating parametric design and additive manufacturing knowledge in industrial design education. Materials Today: Proceedings, 70, 687-693. doi: 10.1016/j.matpr.2022.10.124.
[10] Khoshamadi, N., Banihashemi, S., Poshdar, M., Abbasianjahromi, H., Tabadkani, A., & Hajirasouli, A. (2023). Parametric and generative mechanisms for infrastructure projects. Automation in Construction, 154, article number 104968. doi: 10.1016/j.autcon.2023.104968.
[11] Kwieciński, K., & Słyk, J. (2023). Interactive generative system supporting participatory house design. Automation in Construction, 145, article number 104665. doi: 10.1016/j.autcon.2022.104665.
[12] Miao, L., & Yang, F.X. (2023). Text-to-image AI tools and tourism experiences. Annals of Tourism Research, 102, article number 103642. doi: 10.1016/j.annals.2023.103642.
[13] Oxman, R. (2017). Thinking difference: Theories and models of parametric design thinking. Design Studies, 52, 4-39. doi: 10.1016/j.destud.2017.06.001.
[14] Shi, Y., Shang, M., & Qi, Z. (2023). Intelligent layout generation based on deep generative models: A comprehensive survey. Information Fusion, 100, article number 101940. doi: 10.1016/j.inffus.2023.101940.
[15] Wang, X., Wu, Z., Xiong, Y., Li, Q., & Tao, X. (2023). Fast NURBS-based parametric modeling of human calves with high-accuracy for personalized design of graduated compression stockings. Computer Methods and Programs in Biomedicine, 229, article number 107292. doi: 10.1016/j.cmpb.2022.107292.
[16] Wieja, F., Jacobs, G., Stein, S., Kopp, A., van Gaalen, K., Kröger, N., & Zinser, M. (2022). Development and validation of a parametric human mandible model to determine internal stresses for the future design optimization of maxillofacial implants. Journal of the Mechanical Behavior of Biomedical Materials, 125, article number 104893. doi: 10.1016/j.jmbbm.2021.104893.
[17] Zhang, J., Liu, N., & Wang, S. (2021). Generative design and performance optimization of residential buildings based on parametric algorithm. Energy and Buildings, 244(1), article number 111033. doi: 10.1016/j.enbuild.2021.111033.
[18] Zhang, J., Zhang, K., Peng, R., & Yu, J. (2020). Parametric modeling and generation of mandala thangka patterns. Journal of Computer Languages, 58, srticle number 100968. doi: 10.1016/j.cola.2020.100968.