Moskva, Russian Federation
Moskva, Russian Federation
Moskva, Russian Federation
This study presents and experimentally validates a structural model of multicontour optimization in an intelligent footwear design system. The proposed methodology integrates generative neural network models, materials optimization, and predictive production analysis into a unified architecture of engineering and technological production preparation. The design process is formalized as a multicriteria optimization problem with an integral performance function that accounts for design time, material consumption, defect rate, and the accuracy of geometric reproduction of the finished product. To evaluate the geometric accuracy of transferring the digital model into the physical product, a volumetric shape similarity coefficient was employed, calculated through comparison of the CAD model and the 3D scan of the finished footwear. Experimental validation based on a paired sample of models demonstrated statistically significant improvements compared to the conventional approach: a 50% reduction in design time, a 25% decrease in material waste during cutting, a 50% reduction in production defects, and an increase in shape accuracy from 86.4% to 94.1%. The obtained results confirm the effectiveness of integrating artificial intelligence technologies into the digital design framework and demonstrate the possibility of simultaneous optimization of temporal, material, and geometric characteristics without mutual performance degradation. The proposed methodology can be integrated into existing CAD systems and production lines of mass-segment enterprises.
INTELLIGENT FOOTWEAR DESIGN, MULTICONTOUR OPTIMIZATION, GENERATIVE STRUCTURAL SYNTHESIS, PREDICTIVE PRODUCTION ANALYSIS, COMPUTER VISION, MACHINE LEARNING
1. Rodrigues I. Lumen et virtus, 15. 41, 116-121 (2024). DOI:https://doi.org/10.56238/levv15n41-116. EDN: https://elibrary.ru/NLXINF
2. Yüksel B., Çavdar E. PressAcademia Procedia (PAP), 15, 138-139 (2022). DOI:https://doi.org/10.17261/Pressacademia. 2022.1597. DOI: https://doi.org/10.17261/pressacademia.2022.1597; EDN: https://elibrary.ru/SJDMBH
3. Klarák, J., Kuric, I., Zajačko, I., Bulej, V., Tlach, V., Józwik, J. Sensors, 21. 22, 7531 (2021). DOI:https://doi.org/10.3390/s2122753.1
4. SenGupta K. Emerging Trends in Leather Science and Technology. Springer Nature, Singapore, 2024, P. 251-273.
5. Suessmuth J., Fick F., Van Der Vossen S. ACM SIGGRAPH 2023 Talks (Los Angeles, August 6-10). ACM Press, NY, 2024. P. 11-12. DOI: 10.1145/ 3587421.3595416.
6. Minaoglou, P., Tzotzis, A., Efkolidis, N., Kyratsis, P. Engineering Proceedings, 72. 1, 7 (2024). DOI:https://doi.org/10.3390/engproc2024072007.
7. Giri, C., Jain, S., Zeng, X., & Bruniaux, P. IEEE Access, 7, 95376-95396 (2019). DOI:https://doi.org/10.1109/ACCESS. 2019.2928979.
8. Han X., Sururi L., Luming Y. Journal of the American Leather Chemists Association, 120. 7, 332-344 (2025). DOI:https://doi.org/10.34314/wx9xeq36. EDN: https://elibrary.ru/SGGTKV
9. Rumyantsev, E.V., Rumyantseva, V.E., Konovalova, V.S., Miroshnichenko, D.A. Izvestiya vysshikh uchebnykhzavedenij. Tekhnologiya tekstil'noj promyshlennosti, 5 (413), 5-13 (2024). DOI:https://doi.org/10.47367/0021-3497_2024_5_5. EDN: https://elibrary.ru/ZCGLXV
10. Kopylova I.L., Kiselev S.Yu., Volkova G.Yu. Tekhnologii, dizajn, nauka, obrazovanie v kontekste inklyuzii (Moscow, April 17-19). A.N. Kosygin Russian State University, Moscow, 2018. P. 77-80.
11. Surovtseva O.A. Sovremennye tendencii razvitiya nauki i tekhnologij (Belgorod, October 31). Tkacheva, Belgorod, 2015. Part III. P. 120-122.
12. Surovtseva O.A., Ternavskaya T.V. Izvestiya vysshikh uchebnykh zavedenij. Severokavkazskij region. Tekhnicheskie nauki, 1 (176), 135-138 (2014).
13. Golubeva O.A., Dimitrov V.P., Shvetsov K.A., Doniy A.S.Izvestiya Tul'skogo gosudarstvennogo universiteta. Tekhnicheskie nauki, 10, 103-107 (2024). DOI:https://doi.org/10.24412/2071-6168-2024-10-103-104. EDN: https://elibrary.ru/PXMBYX
14. Karasev D.D., Razin I.B., Karaseva A.I. Kostyleva V.V. Industrial processes and technologies, 4. 2 (12), 32-40 (2024). DOI:https://doi.org/10.37816/2713-0789-2024-4-2(12)-32-40. EDN: https://elibrary.ru/DSCXWD
15. Beshley M., Hordiichuk-Bublivska O., Beshley H., Ivanochko I. Electronics, 12. 1, 33 (2022). DOI:https://doi.org/10.3390/electronics12010033.
16. Xu J., Zhou Z., Wang Z. Sensors, 19. 21, 4708 (2019). DOI:https://doi.org/10.3390/s19214708.
