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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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Fu, Z.-L., Cao, C., & Gao, F. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145533 · Cited by 19
Generative and Predictive AI for digital twin systems in manufacturing
Dai, D., Zhao, B., Yu, Z., Franciosa, P., & Ceglarek, D. J. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1655470 · Cited by 27
Wu, Q., Ding, X., Zhao, L., Liu, R., He, Q., & Shao, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3269122 · Cited by 19
“In recent years, the interpretability of network models has attracted more and more attention from scholars [22], [23], [24].”
BackgroundDevelopment of a Novel Methodology to Retrofit Legacy Systems in the Context of Industry 4.0
Mendonça, R. D. S., de Carvalho, M. M., Machado, G. S., da Silva, C. S. M., de Medeiros, R. L. P., & de Lucena, V. F. (2023) · IEEE Access · doi:10.1109/access.2023.3328537 · Cited by 19
“According to [1] and [2], I4.”
A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models
Jamal, M. H., Naz, N., Khan, M. A., Saeed, F., Altamimi, S. N., & Qasem, S. N. (2023) · IEEE Access · doi:10.1109/access.2023.3332512 · Cited by 19
“With so much data being collected and analyzed, there is a real risk of cyber attacks and data leaks [5].”
BackgroundError homogenization in physics-informed neural networks for modeling in manufacturing
Cooper, C., Zhang, J., & Gao, R. X. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.09.013 · Cited by 17
From Connectivity to Intelligence: The Game-Changing Role of AI and IoT in Industry 5.0
Sharma, M., Tomar, A., & Hazra, A. (2024) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2024.3470340 · Cited by 19
A multi-dimensional hierarchical evaluation system for data quality in trustworthy AI
Zhang, H., Chen, C.-C., Ran, P., Yang, K., Liu, Q.-C., Sun, Z.-Y., Chen, J., & Chen, J. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00999-2 · Cited by 21
“Thus, the evaluation (23) θ 10 = ω 10,1 θ 10,1 + ω 10,2 θ 10,2 + ω 10,3 θ 10,3 + ω 10,4 θ 10,4 + ω 10,5 θ 10,5 + ω 10,6 θ 10,6, dimensions can be defined as θ = {θ 1 , θ 2 , θ 3 , …, θ n }, and n ͼ [1, 10].”
Background(2023) · Issues in Information Systems · doi:10.48009/2_iis_2023_125 · Cited by 24
Enabling Explainable AI in Cybersecurity Solutions
Shah, I. A., Jhanjhi, N. Z., & Ray, S. K. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-6361-1.ch009 · Cited by 18
The Impact of Manufacturing Transformation in Digital Economy Under Artificial Intelligence
Wang, P., Wang, K., Wang, D., & Liu, H. (2024) · IEEE Access · doi:10.1109/access.2024.3396082 · Cited by 19
“Ahmed et al. pointed out that the integration of the IoT and AI in the manufacturing industry could promote the automation and intelligence of the production process, posing a challenge to the traditional manufacturing model [19].”
MethodArtificial intelligence for human–cyber-physical production systems
Mourtzis, D., & Angelopoulos, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13924-6.00012-0 · Cited by 18
Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems
Imam, N. H. (2024) · Sensors · doi:10.3390/s24216891 · Cited by 17
Exploring the role of artificial intelligence on business innovation and entrepreneurship
Singh, R., Khan, S., Joshi, A., Raghuveer, K., & Kumar, V. (2025) · Strategy & Leadership · doi:10.1108/sl-04-2025-0080 · Cited by 17
Fu, M., Wu, J., Wang, Q., Sun, L., Ma, Z., Zhang, C., Guan, W., Li, W., Chen, N., Wang, D., et al. (2023) · IET Signal Processing · doi:10.1049/sil2.12208 · Cited by 17
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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