This research presents a comprehensive hybrid-modeling framework that integrates physics-based simulations with machine learning algorithms to establish a predictive digital twin for smart knitted fabric manufacturing. The system addresses critical challenges in quality prediction, resource optimization, and process parameter control by creating a virtual replica of the entire production chain—from yarn input to finished fabric. The framework consists of three interconnected modules: a physics-based finite element model simulating yarn mechanics and loop formation dynamics during knitting; a data-driven deep learning module trained on historical production data to predict defect occurrence probability based on real-time sensor inputs; and an optimization engine using multi-objective genetic algorithms to balance competing production objectives including quality, speed, and resource consumption. The digital twin was implemented and validated over six months in a pilot production facility using four LONG XING SM-252 flat knitting machines producing technical knitted fabrics. Results demonstrate unprecedented predictive capabilities: the system achieved 94.7% accuracy in predicting defect occurrences 15 minutes before manifestation, enabling proactive intervention. Process parameter optimization reduced yarn waste by 23.8% while maintaining product quality standards with defect rates below 0.5%. Energy consumption decreased by 18.2% through optimized machine scheduling and parameter adjustments. The integration of IoT sensors including tension, vibration, thermal and visual sensors provided real-time data streams updating the digital twin at 1-second intervals. Comparative analysis against traditional statistical process control methods showed a 67.3% reduction in quality-related production stoppages and a 41.5% improvement in overall equipment effectiveness. This work establishes a practical roadmap for Industry 4.0 transformation in textile manufacturing, demonstrating how digital twin technology can bridge the gap between theoretical process understanding and practical production optimization, ultimately creating more sustainable, efficient, and quality-conscious manufacturing systems.

Digital Twin, Smart Manufacturing, Predictive Quality Control

Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869.

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415.

Kusiak, A. (2018). Smart manufacturing must embrace big data. Nature, 544(7648), 23-25.

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.

Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems (pp. 85-113). Springer, Cham.

Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, 103130.

Uhlemann, T. H. J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017). The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia CIRP, 61, 335-340.

Kyosev, Y. (Ed.). (2019). Advances in knitting technology. Woodhead Publishing.

Wang, J., Ye, L., Gao, R. X., Li, C., & Zhang, L. (2019). Digital twin for rotating machinery fault diagnosis in smart manufacturing. International Journal of Production Research, 57(12), 3920-3934.

Zhang, H., Liu, Q., Chen, X., Zhang, D., & Leng, J. (2017). A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access, 5, 26901-26911.

Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422-440.

Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686-707.

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.

Korabayev, S., Bobojanov, H., Sharibaev, N., Sadikov, M., Salayeva, N., Valieva, Z., & Makhkamova, S. (2025). Real-time monitoring of garment pressure and strain by integrating FBG sensors into knitted fabric. Proceedings of SPIE - the International Society for Optical Engineering, 46. https://doi.org/10.1117/12.3094148

Oxunov et al., (2024). Research on the effect of the type of raw material on the physical and mechanical properties of the fabric obtained by the circular needle jacquard knitting machine. AIP Conference Proceedings, 3045, 030046. https://doi.org/10.1063/5.0197292