Abstract

The rapid evolution of Industry 4.0 and the emerging transition toward Industry 5.0 have been catalyzed by the convergence of intelligent digital technologies such as digital twins, cyber–physical systems (CPS), artificial intelligence (AI), the Internet of Things (IoT), and human-in-the-loop (HITL) frameworks. These technologies have transformed traditional manufacturing into adaptive, data-centric ecosystems capable of real-time optimization and predictive decision-making. In recent years, the fusion of computer numerical control (CNC) machines, nanocellulose-based smart materials, and cloud-enabled manufacturing analytics has expanded the operational intelligence of smart factories. Additionally, the advent of AI-generated content (AIGC) and ontology-driven architectures has introduced new paradigms for knowledge representation, semantic interoperability, and self-adaptive industrial systems Despite these advancements, a significant research gap persists in integrating multi-modal sensing, digital twin-assisted learning environments, and agentic workflow automation into a unified, interoperable framework. Addressing this gap is crucial for realizing holistic industrial intelligence, where data, algorithms, and human expertise co-evolve symbiotically. This paper proposes a multi-layered framework that harmonizes software–hardware co-design, adaptive material intelligence, and collaborative human–machine interactions to enhance manufacturing resilience and sustainability. The proposed framework bridges industrial-scale foundation models with adaptive sensing networks and real-time control architectures, creating a scalable infrastructure for intelligent decision-making, energy-efficient operations, and agile supply-chain management. Through this integration, the study outlines pathways for embedding contextual awareness, self-learning mechanisms, and cognitive adaptability within industrial ecosystems. Ultimately, this approach extends beyond the automation-driven logic of Industry 4.0, envisioning an Industry 5.0 paradigm where human creativity, sustainability, and ethical intelligence are central to manufacturing innovation. The findings and conceptual model presented herein aim to guide future research toward developing resilient, human-centric, and sustainable smart factories powered by continuous human–machine collaboration.

Keyword: Smart manufacturing; industry 4.0; digital twin; human-in-the-loop; cyber-physical systems; AIGC; nano cellulose packaging; multi- sensor fusion; intelligent decision-making; sustainable production; industry 5.0

Full Text PDF

Refrences:

1. Ali, M., Salah, B., & Habib, T. (2024). Utilizing industry 4.0-related technologies and modern
techniques for manufacturing customized products – Smart yogurt filling system. Journal of
Engineering Research (Kuwait), 12(3), 468–475. https://doi.org/10.1016/j.jer.2023.100144
2. Ashish, S., Karanam, K., & Hartman, N. W. (2025). Manufacturing Letters A Systematic Review
of Digital Twin (DT) and Virtual Learning Environments (VLE) for Smart Manufacturing
Education. NAMRC, 53. www.sciencedirect.com
3. Dachepalli, V., & Gavini, S. (2025). A Virtually assisted digital twin enabled object detection in
smart industrial manufacturing. Expert Systems with Applications, 294.
https://doi.org/10.1016/j.eswa.2025.128574
4. FU, S., ANTWI-AFARI, M. F., ANWER, S., CHEN, Z.-S., & LI, H. (2025). A state-of-the-art
review of digital twin-enabled human-robot collaboration in smart energy management systems.
Results in Engineering, 106524. https://doi.org/10.1016/j.rineng.2025.106524
5. Gao, T., Wang, L., Song, W., Cheng, Y., Zuo, Y., Xiang, F., Zhang, H., & Tao, F. (2025). Ten
industrial software towards smart manufacturing. In Journal of Manufacturing Systems (Vol. 79,
pp. 255–285). Elsevier B.V. https://doi.org/10.1016/j.jmsy.2025.01.012
6. Hou, Y., Wu, Z., Qin, W., Loy, D. A., & Lin, D. (2025). A review on surface and interface
engineering of nanocellulose and its application in smart packaging. In Advances in Colloid and
Interface Science (Vol. 345). Elsevier B.V. https://doi.org/10.1016/j.cis.2025.103645
7. Kim, D. B., Bajestani, M. S., Lee, J. Y., Shin, S. J., Kim, G. Y., Sajadieh, S. M. M., & Noh, S.
(2025). Human-in-the-loop in smart manufacturing (H-SM): A review and perspective. Journal of
Manufacturing Systems, 82, 178–199. https://doi.org/10.1016/j.jmsy.2025.05.020

8. Leng, J., Zheng, K., Li, R., Chen, C., Wang, B., Liu, Q., Chen, X., & Shen, W. (2026). AIGC-
empowered smart manufacturing: Prospects and challenges. Robotics and Computer-Integrated

Manufacturing, 97. https://doi.org/10.1016/j.rcim.2025.103076
9. Longo, F., Mirabelli, G., Nicoletti, L., & Solina, V. (2022). An ontology-based, general-purpose
and Industry 4.0-ready architecture for supporting the smart operator (Part I – Mixed reality case).
Journal of Manufacturing Systems, 64, 594–612. https://doi.org/10.1016/j.jmsy.2022.08.002

Integrating Digital Twins, Smart Materials, and Human Sandeep Kumar Chouksey

© STM Journals 2025. All Rights Reserved 8
10. Lou, Y., Ouyang, Y., Xie, H., Tian, H., Li, S., Wu, M., & Song, X. (2025). Smart Pickering
emulsions stabilized by functionalized nanoparticles: Innovative applications in advanced food
packaging. In Advances in Colloid and Interface Science (Vol. 346). Elsevier B.V.
https://doi.org/10.1016/j.cis.2025.103673
11. Manufacturing Letters. (2025). www.sciencedirect.com
12. Martins, A., Lucas, J., Costelha, H., & Neves, C. (2023). CNC Machines Integration in Smart
Factories using OPC UA. Journal of Industrial Information Integration, 34.
https://doi.org/10.1016/j.jii.2023.100482
13. Meng, Q., Wang, T., Li, S., Xie, H., Tian, H., Wu, M., & Song, X. (2025). Regulating dispersion
of OEO Pickering emulsion to construct smart antibacterial nanocellulose packaging films
contained microcapsule. Food Chemistry, 492. https://doi.org/10.1016/j.foodchem.2025.145547
14. Revathi, D., Panda, S., Deshmukh, K., Khotele, N., Murthy, V. R. K., & Pasha, S. K. K. (2025).
Smart hydrogels for sensing and biosensing – Preparation, smart behaviours, and emerging
applications – A comprehensive review. In Polymer Testing (Vol. 150). Elsevier Ltd.
https://doi.org/10.1016/j.polymertesting.2025.108912
15. Singh, J., Singh, A., Singh, H., & Doyon-Poulin, P. (2025). Implementation and evaluation of a
smart machine monitoring system under industry 4.0 concept. Journal of Industrial Information
Integration, 43. https://doi.org/10.1016/j.jii.2024.100746
16. Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a
review. In Internet of Things and Cyber-Physical Systems (Vol. 3, pp. 192–204). KeAi
Communications Co. https://doi.org/10.1016/j.iotcps.2023.04.006
17. Sun, B., Zhang, C., Li, X., Zhong, L., Zhang, Z., & Zhou, D. (2025). Nanocellulose-based hydrogels
for smart sensors. In Carbohydrate Polymers (Vol. 368). Elsevier Ltd.
https://doi.org/10.1016/j.carbpol.2025.124152

18. Tian, Z., Li, S., Zou, S., Li, H., Zou, Z., Zhang, X., & Tang, Q. (2025). Colorimetric ammonia-
sensitive potato starch-based smart films containing cellulose nanocrystal and Fe-ICA complex for

shrimp freshness monitoring. Industrial Crops and Products, 236.
https://doi.org/10.1016/j.indcrop.2025.121940
19. Tnani, M. A., Feil, M., & Diepold, K. (2022). Smart Data Collection System for Brownfield CNC
Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring. Procedia
CIRP, 107, 131–136. https://doi.org/10.1016/j.procir.2022.04.022
20. Zhang, J., Li, C., Deng, C., Luo, T., Deng, R., Luo, D., Tao, G., & Cao, H. (2025). Toward digital

twins for intelligence manufacturing: Self-adaptive control in assisted equipment through multi-
sensor fusion smart tool real-time machine condition monitoring. Journal of Manufacturing

Systems, 82, 301–318. https://doi.org/10.1016/j.jmsy.2025.06.020
21. Zhao, W., Chen, M., Xia, W., Xi, X., Zhao, F., & Zhang, Y. (2020). Reconstructing CNC platform
for EDM machines towards smart manufacturing. Procedia CIRP, 95, 161–177.
https://doi.org/10.1016/j.procir.2020.03.134
22. Zheng, K., Zhong, Y., Su, X., Leng, J., Liu, Q., & Chen, X. (2025). Towards Agentic Smart Design:
An Industrial Large Model-driven Human-in-the-loop Agentic Workflow for Geometric Modelling.
Applied Soft Computing, 113920. https://doi.org/10.1016/j.asoc.2025.113920