Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 03 | Page :
    By

    Makrand V. Kulkarni,

  • Kalpana G. Joshi,

  • Shanthi Kumaraguru,

  • Vinit Kotak,

  • Prashant V. Thokal,

  • Prashant M. Yawalkar,

  • Vijay More,

  1. Assistant Professor, Department of Information Technology, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  2. Assistant Professor, School of Engineering and Technology, Sanjivani University, Kopargaon, Maharashtra, India
  3. Assistant Professor, Department of Information Technology, D Y Patil College of Engineering, Akurdi, Pune, Maharashtra, India
  4. Professor, Department of Electrical Engineering, Shah & Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India
  5. Assistant Professor, Department of Electrical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  6. Associate Professor, Department of Computer Engineering, MET’s Institute of Engineering, BKC, Nashik, Maharashtra, India
  7. Associate Professor, Department of Computer Engineering, MET’s Institute of Engineering, BKC, Nashik, Maharashtra, India

Abstract

The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with advanced data-centric modeling techniques. A comprehensive digital twin framework is developed to accurately replicate the physical and thermomechanical behavior of polymer processing, establishing a virtual counterpart that mirrors real-world manufacturing conditions with high fidelity.

Ensemble machine learning models—namely Random Forest and Gradient Boosting—are employed for predictive analysis and benchmarked against one another to determine the most effective modeling strategy. The models are trained on a synthetic dataset comprising 500 instances across six widely used polymer categories (PE, PP, PVC, PET, PA, and PS), incorporating eight significant process variables that capture the multidimensional nature of polymer behavior. Performance evaluation reveals that the Gradient Boosting model outperforms competing approaches, achieving an R² of 0.9101, a Mean Absolute Error of 6.61 MPa, and a Root Mean Square Error of 8.20 MPa, collectively indicating strong predictive capability and generalization.Feature importance analysis further highlights molecular weight, crystallinity, and processing temperature as the primary factors governing tensile strength outcomes. The proposed system enables continuous monitoring and real-time prediction within dynamic manufacturing environments, thereby supporting proactive process adjustments and improved product quality control. Overall, this work advances the evolution of smart manufacturing under the Industry 4.0 paradigm by seamlessly integrating physical and digital systems to enable data-driven optimization and proactive control of material properties.

Keywords: Digital Twin Integration; Machine Learning Models; Polymeric Strength Prediction; Real-Time Processing; Gradient Boosting Regression; Random Forest Model; Smart Manufacturing; Materials Data Science.

How to cite this article:
Makrand V. Kulkarni, Kalpana G. Joshi, Shanthi Kumaraguru, Vinit Kotak, Prashant V. Thokal, Prashant M. Yawalkar, Vijay More. Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Makrand V. Kulkarni, Kalpana G. Joshi, Shanthi Kumaraguru, Vinit Kotak, Prashant V. Thokal, Prashant M. Yawalkar, Vijay More. Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=244257


References

[1] Y. Yang et al., “Machine learning-driven digital twin for strength prediction of dissimilar adhesive joints under environmental aging,” Journal of Composite Materials, 2025, doi: 10.1177/00219983251342147.
[2] M. Karimi, “Adaptive digital twin framework for monitoring and predicting the performance of composite adhesive joints,” Journal of Adhesion, 2025, doi: 10.1080/00218464.2025.2535687.
[3] A. Hürkamp et al., “Combining simulation and machine learning as digital twin for the manufacturing of overmolded thermoplastic composites,” Journal of Manufacturing and Materials Processing, vol. 4, no. 3, p. 92, 2020, doi: 10.3390/JMMP4030092.
[4] J. Butt et al., “Combining digital twin and machine learning for the fused filament fabrication process,” Superalloys, 2022, doi: 10.3390/met13010024.
[5] M. A. Khan et al., “Integration of machine learning and digital twin in additive manufacturing of polymeric-based materials and products,” Progress in Additive Manufacturing, 2025, doi: 10.1007/s40964-025-01257-4.
[6] M. Suess et al., “Polyester resin–quartz composites in the age of artificial intelligence and digital twins,” 2025.
[7] A. Aysa et al., “Integrating machine learning and digital twin for strength prediction of CFRP/aluminum adhesive joints under hygrothermal conditions,” Polymer Composites, 2025, doi: 10.1002/pc.29928.
[8] P. K. Mishra et al., “Machine learning-assisted pattern recognition algorithms for estimating ultimate tensile strength in fused deposition modelled polylactic acid specimens,” Materials Technology, 2023, doi: 10.1080/10667857.2023.2295089.
[9] Y. Ji et al., “Experimental study on the mechanical property of carbon fiber reinforced polymer with the combined application of digital twin and reduced order algorithm,” Polymer Composites, vol. 45, 2024, doi: 10.1002/pc.28505.
[10] Y. Zhan et al., “Transfer learning for polymer mechanics: A fusion approach to bridge molecular dynamics simulations and experiments in SSBR,” Macromolecular Rapid Communications, 2025, doi: 10.1002/marc.202500386.
[11] B. S. Sindu et al., “Feature-based prediction of properties of cross-linked epoxy polymers by molecular dynamics and machine learning techniques,” Modelling and Simulation in Materials Science and Engineering, 2025, doi: 10.1088/1361-651x/adf56c.
[12] A. Menon et al., “Hierarchical machine learning model for mechanical property predictions of polyurethane elastomers from small datasets,” Frontiers in Materials, vol. 6, p. 87, 2019, doi: 10.3389/FMATS.2019.00087.
[13] Y. Wang et al., “Digital twin modeling for structural strength monitoring via transfer learning-based multi-source data fusion,” Mechanical Systems and Signal Processing, vol. 200, 2023, doi: 10.1016/j.ymssp.2023.110625.
[14] A. Khayyami, “Predicting mechanical properties of polymer films after extrusion coating using supervised machine learning algorithms,” 2019.
[15] S. Stieber et al., “Towards real-time process monitoring and machine learning for manufacturing composite structures,” in Proc. IEEE Int. Conf. ETFA, 2020, pp. 1–8, doi: 10.1109/ETFA46521.2020.9212097.
[16] J. Huang et al., “A machine learning framework to predict the tensile stress of natural rubber: Based on molecular dynamics simulation data,” Polymers, vol. 14, no. 9, p. 1897, 2022, doi: 10.3390/polym14091897.
[17] F. S. Utku et al., “Prediction of mechanical properties of synthetic waste reinforced polyolefins with GA-LSTM hybrid model,” doi: 10.18586/msufbd.1535577.
[18] A. Cravero et al., “Improving predictive modeling of polymeric materials using a hybrid approach of machine learning and expert intervention,” 2023, doi: 10.18687/laccei2023.1.1.1572.
[19] Y. Zhou et al., “Multimodal machine learning with 3D-weighted-matrix encoding for high-throughput design of high-performance polyurethanes,” Macromolecular Rapid Communications, 2025, doi: 10.1002/marc.202500471.
[20] L. Xu et al., “Digital twin and cross-scale mechanical interaction for fabric rubber composites considering model uncertainties,” Composites Science and Technology, vol. 255, 2024, doi: 10.1016/j.compscitech.2024.110431.
[21] Z. Yang et al., “Physics augmented machine learning discovery of composition-dependent constitutive laws for 3D printed digital materials,” arXiv preprint, 2025, doi: 10.48550/arxiv.2507.02991.
[22] A. Anvari et al., “A digital twin and machine learning approach for real-time fatigue life prediction of CFRP-to-aluminum adhesive joints subjected to environmental aging,” The International Journal of Advanced Manufacturing Technology, 2025, doi: 10.1007/s00170-025-16042-4.
[23] J. Gyabaah et al., “Machine learning-assisted polymer and polymer composite design for additive manufacturing,” 2025.
[24] S. Palanisamy et al., “Wear properties and post-moisture absorption mechanical behavior of kenaf/banana-fiber-reinforced epoxy composites,” Fibers, vol. 10, no. 4, p. 32, 2022, doi: 10.3390/fib10040032.
[25] K. Aruchamy et al., “Enhancement of mechanical properties of hybrid polymer composites using palmyra palm and coconut sheath fibers: The role of tamarind shell powder,” BioResources, vol. 20, no. 1, pp. 698–724, 2025, doi: 10.15376/biores.20.1.698-724.
[26] N. Ayrilmis et al., “Utilizing waste manhole covers and fibreboard as reinforcing fillers for thermoplastic composites,” Journal of Reinforced Plastics and Composites, vol. 44, no. 17–18, pp. 1108–1118, 2024, doi: 10.1177/07316844241238507.
[27] R. Ramasubbu et al., “Mechanical properties of epoxy composites reinforced with Areca catechu fibers containing silicon carbide,” BioResources, vol. 19, no. 2, pp. 2353–2370, 2024, doi: 10.15376/biores.19.2.2353-2370.
[28] S. Palanisamy, T. M. Murugesan, M. Palaniappan, C. Santulli, and N. Ayrilmis, “Fostering sustainability: The environmental advantages of natural fiber composite materials – a mini review,” Environmental Research and Technology, vol. 7, no. 2, pp. 256–269, 2024, doi: 10.35208/ert.1397380.

Ahead of Print Subscription Original Research
Volume 14
03
Received 18/04/2026
Accepted 08/05/2026
Published 18/05/2026
Publication Time 30 Days
8

Login


My IP

PlumX Metrics