Research on the design of biomimetic cultural and creative products driven by mechanical force
Abstract
The incorporation of biomimetic ideas into product design has emerged as a viable strategy for increasing creativity and utility in cultural and creative products. This study focuses on the design of a household appliance, especially a cultural one, by substituting standard materials with novel alternatives powered by mechanical forces. The objective of this research is to provide a thorough framework for assessing the quality of the newly constructed home appliance by utilizing a unique technique called Adaptable Pelican optimization fine-tuned Gradient boosting machine (APO-GBM). We apply powerful machine learning techniques to predict product quality by identifying essential features like design quality, durability, and user satisfaction. The results show that the application of mechanical forces increases the vessels’ functional efficiency and durability in addition to improving their appearance. The hybrid model is highly accurate in forecasting product quality, opening the path for future advances in biomimetic design. The study’s findings highlight the possibility of combining mechanical forces with biomimetic concepts to produce unique, cultural, and creative products. This information may be very helpful to manufacturers and designers who want to improve the sustainability and quality of their products.
References
1. Kam, S., 2021. Three-dimensional printing fashion product design with emotional durability based on Korean aesthetics. Sustainability, 14(1), p.240.
2. Lucibello, S. and Rotondi, C., 2024. Bio-digital ‘Material Systems’: New Hybrid Ways for Material-Driven Design Innovation. In Biomimetics, Biodesign and Bionics: Technological Advances Toward Sustainable Development (pp. 37-68). Cham: Springer Nature Switzerland.
3. Langella, C., 2024. Levels of Access to Biomimetics. In Biomimetics, Biodesign and Bionics: Technological Advances Toward Sustainable Development (pp. 227-250). Cham: Springer Nature Switzerland.
4. Candia Carnevali, M.D., Sugni, M., Bonasoro, F. and Wilkie, I.C., 2024. Mutable Collagenous Tissue: A Concept Generator for Biomimetic Materials and Devices. Marine Drugs, 22(1), p.37.
5. Purwaningsih, R., Nurkertamanda, D., Ulkhaq, M.M., Azzahra, F. and Musyaffa, D.A., 2024. Designing nature-inspired swimming gloves: a biomimicry design spiral approach. Cogent Engineering, 11(1), p.2316468.
6. Hayes, S., Desha, C. and Baumeister, D., 2020. Learning from nature–Biomimicry innovation to support infrastructure sustainability and resilience. Technological Forecasting and Social Change, 161, p.120287.
7. Medina, M.A., Haglund, A.V., Herniández, J.M.H. and Ramírez, J.T., 2023. The Era of Human-Machine Hybrid Medical Advances in Biomimetic Devices. In Nanochemistry (pp. 413-423). CRC Press.
8. Ramesh, S., Deep, A., Tamayol, A., Kamaraj, A., Mahajan, C. and Madihally, S., 2024. Advancing 3D bioprinting through machine learning and artificial intelligence. Bioprinting, p.e00331.
9. Barbinta-Patrascu, M.E., Bita, B. and Negut, I., 2024. From Nature to Technology: Exploring the Potential of Plant-Based Materials and Modified Plants in Biomimetics, Bionics, and Green Innovations. Biomimetics, 9(7), p.390.
10. Ighodaro, A., Osarobo, J.A., Onuguh, I.C., Ogbeide, O.K. and Ifijen, I.H., 2024, February. Challenges and Future Perspectives of Biomimetic Materials for Biomedical Applications: Bridging the Gap Between Nature and Medicine. In TMS Annual Meeting & Exhibition (pp. 877-896). Cham: Springer Nature Switzerland.
11. Lee, J., Oh, S.J., An, S.H., Kim, W.D. and Kim, S.H., 2020. Machine learning-based design strategy for 3D printable bioink: elastic modulus and yield stress determine printability. Biofabrication, 12(3), p.035018.
12. Sippach, T., Dahy, H., Uhlig, K., Grisin, B., Carosella, S. and Middendorf, P., 2020. Structural optimization through biomimetic-inspired material-specific application of plant-based natural fiber-reinforced polymer composites (NFRP) for future sustainable lightweight architecture. Polymers, 12(12), p.3048.
13. Ng, W.L., Chan, A., Ong, Y.S. and Chua, C.K., 2020. Deep learning for fabrication and maturation of 3D bio-printed tissues and organs. Virtual and Physical Prototyping, 15(3), pp.340-358.
14. MacKinnon, R.B., Oomen, J. and Pedersen Zari, M., 2020. Promises and presuppositions of biomimicry. Biomimetics, 5(3), p.33.
15. Ellery, A., 2021. Are there biomimetic lessons from genetic regulatory networks for developing a lunar industrial ecology? Biomimetics, 6(3), p.50.
16. Sun, J., Yao, K., Huang, K. and Huang, D., 2022. Machine learning applications in scaffold-based bioprinting. Materials Today: Proceedings, 70, pp.17-23.
17. Chaturvedi, I., Jandyal, A., Wazir, I., Raina, A. and Haq, M.I.U., 2022. Biomimetics and 3D printing-Opportunities for design applications. Sensors International, 3, p.100191.
18. Liu, S., Yu, J.M., Gan, Y.C., Qiu, X.Z., Gao, Z.C., Wang, H., Chen, S.X., Xiong, Y., Liu, G.H., Lin, S.E. and McCarthy, A., 2023. Biomimetic natural biomaterials for tissue engineering and regenerative medicine: new biosynthesis methods, recent advances, and emerging applications. Military Medical Research, 10(1), p.16.
19. Sun, F., Xu, H., Meng, Y. and Lu, Z., 2023. A BERT-based model for coupled biological strategies in biomimetic design. Neural Computing and Applications, 35(3), pp.2827-2843.
20. Santoianni, F., 2024. Biomimetic Learning Design in the Artificial Era. In Mind, Body, and Digital Brains (pp. 17-28). Cham: Springer Nature Switzerland.
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