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From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where
Abstract
Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications.
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From Explainable AI: enhancing decision-making in the detection of cyber threats (2026) · cites this paper as Ahmed et al., 2022
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Yang, N. D., & Liu, H. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3386622 · Cited by 9
Design of ultra-broadband long-wave to ultra-long-wave infrared absorber based on machine learning
Ali, R., Su, W., Ding, Z., Ali, M., Ismail, H., Saif, Z., Ali, J., & Yao, H. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad897e · Cited by 9
Resource Management and Optimization in Internet of Vehicles for Hierarchical Federated Learning
Yuan, T., Chen, L., Jiang, Y., Chen, H., Gong, W., & Gu, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3486775 · Cited by 11
“With the rapid development of artificial intelligence (AI) and wireless networks, the deep integration of internet of vehicles (IoV) and AI has become a significant trend driving the data-driven development of IoV [1], [2], [3], [4].”
BackgroundExplainable artificial intelligence for machine learning prediction of bandgap energies
Masuda, T., & Tanabe, K. (2024) · Journal of Applied Physics · doi:10.1063/5.0226151 · Cited by 9
Murugesan, G. S., & Viswanathan, S. A. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724819 · Cited by 11
“Responsible deployment involves ensuring that AI algorithms are transparent, accountable, and fair, avoiding biases and ensuring equitable outcomes as mentioned in [1].”
BackgroundMolitor, D. A., Kokozinski, A., Kubik, C., Arne, V., Veitenheimer, C.-V., Georgi, F., Krämer, R., & Groche, P. (2025) · Production Engineering · doi:10.1007/s11740-024-01328-5 · Cited by 9
“Such human-in-the-loop approaches are currently being researched and will be incorporated into the manufacturing sector in the future [26].”
Hirsi, A., Audah, L., Salh, A., Alhartomi, M. A., Sun, Z.-L., Hammoodi, A., & Ahmed, S. (2025) · Physical Communication · doi:10.1016/j.phycom.2025.102712 · Cited by 11
Yu, Y., Karimi, H. R., & Pawełczyk, M. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113674 · Cited by 9
Federated-Learning-Based Synchrotron X-Ray Microdiffraction Image Screening for Industry Materials
Chen, B., Xu, K., Zhu, Y., Tian, L., & Chang, V. I. C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3205372 · Cited by 9
“In [31], a survey was done on the explainable artificial intelligence in industry 4.”
BackgroundAAPP: An Accelerative and Adaptive Path Planner for Robots on GPU
Liu, J., Xiao, G., Wu, F., Liao, X., & Li, K. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3248274 · Cited by 9
“field, Pictobot, a spraying robot, can automatically spray walls at high altitude [5], [6].”
BackgroundDeveloping an explainable hybrid deep learning model in digital transformation: an empirical study
Chiu, M., Chiang, Y.-H., & Chiu, J.-E. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02127-y · Cited by 9
Batra, I. (2024) · Journal of Electrical Systems · doi:10.52783/jes.2000 · Cited by 10
“Potential solutions include implementing regulations to protect personal information, developing secure communication protocols, and using XAI techniques to increase the openness and interpretability of decisions made by CPS [50].”
MethodRandom convolution layer: an auxiliary method to improve fault diagnosis performance
Zhao, Z., Zhao, R., & Jiao, Y. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02458-4 · Cited by 8
“With the emergence of Industry 4.0, the transformation of traditional industries to intelligence and information technology has become a major focus of research (Ahmed et al., 2022).”
InfluentialGraph neural network-based anomaly detection for human cyber physical systems
Xue, C., Lin, L., Huang, Y., & Wang, X. (2024) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2024.2407294 · Cited by 8
“HCPS incorporates human elements into the traditional framework of cyber physical systems (CPS) (Yu et al. 2023), which is a product of the Industry 4.0 (Ahmed, Jeon, and Piccialli 2022) phase.”
InfluentialBlair, J., Amin, O., Brown, B. D., McArthur, S. D. J., Forbes, A., & Stephen, B. (2024) · Data-Centric Engineering · doi:10.1017/dce.2024.54 · Cited by 8
“…on a local or global scale, where global explanations provide an overview of the models learned relationship between all input variable instances and resulting output predictions, and local explanations provide insight into the contributions of one observation or region in the model space [21].”
Olan, F., Παπαδόπουλος, Θ., Spanaki, K., & Jayawickrama, U. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10583-5 · Cited by 15
“To achieve this goal, a socio-technically informed approach was introduced and explored (Chen et al., 2019), one that takes into account the socio-organizational setting (Ahmed et al., 2022; Bonamigo & Frech, 2020).”
Recent advances in multimodal mechanoluminescent sensors enabled by nanostructure design
Wang, Z., Hu, J., Yang, M., Liu, J., & Zhang, X. (2025) · Nanoscale · doi:10.1039/d4nr04875j · Cited by 8
Muñoz-Ordóñez, J. F., Cobos, C., Vidal-Rojas, J.-C., & Herrera, F. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/4934696 · Cited by 12
Priporas, C., Hussain, S., & Rahman, H. (2025) · International Marketing Review · doi:10.1108/imr-10-2024-0433 · Cited by 8
Metadata
- Authors
- Ahmed, I., Jeon, G., & Piccialli, F.
- Affiliations
- Institute of Management Sciences Peshawar (PK)
- Incheon National University (KR)
- University of Naples Federico II (IT)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-08
- Volume
- 18
- Issue
- 8
- Pages
- 5031-5042
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4210725851
- Cited by
- 853
- References
- 88
- Citations by year
- 2026: 163 · 2025: 246 · 2024: 249 · 2023: 149 · 2022: 46
- FWCI
- 56.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- 4I: mixed reality, machine learning, gamification and educational for Industry (F/190130/01-03/X44)
- Fondo per la Crescita Sostenibile—Sportello
- Fabbrica Intelligente
- PON I & C (CUP: B66G21000040005, COR: 4641138)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- explainable AI, Industry 4.0, machine learning, deep learning, predictive maintenance, downtime reduction, responsible AI, human-centered AI, intelligent machines, industrial transformation
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