Keywords
Intelligent Sensing
Causal Inference
Abstract
The convergence of deep learning, graph neural networks, large language models (LLMs), and physics-informed modeling is fundamentally reshaping industrial systems across perception, reasoning, and decision-making dimensions. This review synthesizes findings from five representative studies spanning optical metrology, thermal imaging, gesture-based robotic control, automated software testing, and causal-aware supply chain forecasting. We systematically examine three transformative research trajectories: (1) the integration of deep learning with traditional physics-based sensing paradigms, (2) the incorporation of causal inference into time-series forecasting for complex networked systems, and (3) the emergence of natural interaction modalities that democratize human-machine collaboration. By cross-referencing the five focal papers with a broader corpus of 11 additional peer-reviewed works, we identify converging themes, open challenges, and promising future directions. This review contributes a unified analytical framework that connects intelligent sensing with intelligent analytics and offers actionable insights for researchers and practitioners navigating the ongoing intelligence transformation of industrial systems.
References
Cai, S., et al. (2025). Physics-informed machine learning in design and manufacturing: Status and challenges. *Journal of Computing and Information Science in Engineering*, 25(12). https://doi.org/10.1115/1.4050000
Chen, M., et al. (2025). Development of gesture control robotic arm experimental platform based on Leap Motion. In *Proceedings of the 2025 2nd International Conference on Informatics Education and Computer Technology Applications*. ACM. https://doi.org/10.1145/3732801.3732844
Chen, Y., et al. (2025). Hierarchical attention-driven dynamic graph neural networks for accurate supply chain demand forecasting. In *Lecture Notes in Mechanical Engineering*. Springer. https://doi.org/10.1007/978-981-95-0009-3_40
Chuang, C.-H., Chen, M.-C., & Chen, Y.-L. (2018). Portable and contactless gesture interface for controlling collaborative robots. *IEEE Transactions on Industrial Electronics*, 65(12), 9715–9724. https://doi.org/10.1109/TIE.2018.2823666
Ciampi, F. G., Rega, A., Diallo, T. M. L., & Patalano, S. (2025). Physics-informed neural networks for industrial applications: A case study in thermal power prediction of an AHU for a topcoat process. In *Lecture Notes in Mechanical Engineering* (pp. 389–400). Springer. https://doi.org/10.1007/978-3-031-76597-1_36
Feldt, R., Torkar, R., Gousios, G., & Abdalkareem, R. (2018). Challenges and practices at the intersection of software engineering and artificial intelligence: A systematic literature review. *arXiv preprint arXiv:1811.02832*. https://doi.org/10.48550/arXiv.1811.02832
Guo, L., et al. (2025). A hybrid learning framework for forecasting uncertainty and adaptive inventory planning in retail supply chains. *ScienceDirect*. https://doi.org/10.1016/j.iforecast.2025.01.005
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. *Neural Computation*, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang, H., Tang, J., Liu, T., & Huang, M.-L. (2026). Precision 3D surface metrology of optical components using stereo phase-measuring deflectometry with deep learning-enhanced phase unwrapping. *Proceedings of SPIE*, 0898. https://doi.org/10.1117/12.3093993
Huang, H., Yang, Y., & Zhu, Y. (2023). Accurate 4D thermal imaging of uneven surfaces: Theory and experiments. *International Journal of Heat and Mass Transfer*, 211, 124580. https://doi.org/10.1016/j.ijheatmasstransfer.2023.124580
Job, F. (2025). Exploring causal learning through graph neural networks: An in-depth review. *WIREs Data Mining and Knowledge Discovery*, e70024. https://doi.org/10.1002/widm.70024
Li, Y., Lou, J., Cai, Z., Zheng, P., Wu, H., & Wang, X. (2024). An interactive gesture control system for collaborative manipulator based on Leap Motion Controller. *Advances in Mechanical Engineering*, 16(5), 16878132241253101. https://doi.org/10.1177/16878132241253101
Liu, K., et al. (2021). Segmentation phase measuring deflectometry for measuring structured specular surfaces. *International Journal of Advanced Manufacturing Technology*, 117, 1987–1998. https://doi.org/10.1007/s00170-021-08439-8
LLM-Testing. (2025). LLM4SoftwareTesting [GitHub repository]. https://github.com/LLM-Testing/LLM4SoftwareTesting
Pearl, J. (2009). *Causality: Models, reasoning, and inference* (2nd ed.). Cambridge University Press.
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. https://doi.org/10.1016/j.jcp.2018.10.045
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. In *Advances in Neural Information Processing Systems* (NeurIPS) (pp. 5998–6008). Curran Associates, Inc.
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2018). Graph attention networks. In *International Conference on Learning Representations (ICLR)*. https://doi.org/10.48550/arXiv.1710.10903
Wang, S., Yu, Y., Feldt, R., & Parthasarathy, D. (2025). Automating a complete software test process using LLMs: An automotive case study. In *2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE)*. https://doi.org/10.1109/ICSE55347.2025.00211
Zhu, Y., & Liu, Q. (2026). Hybrid graph attention network-LSTM models for causal-aware supply chain forecasting. *Journal of Intelligent Manufacturing*. https://doi.org/10.1007/s10845-025-02782-3
S. Wang, Y. Yu, R. Feldt and D. Parthasarathy, "Automating a Complete Software Test Process Using LLMs: An Automotive Case Study," 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE), Ottawa, ON, Canada, 2025, pp. 373-384, doi: 10.1109/ICSE55347.2025.00211.