From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Ahmed, Imran; Jeon, Gwanggil; Piccialli, Francesco

doi:10.1109/tii.2022.3146552

Cited by 854Explainable Artificial Intelligence (XAI)Impact of AI and Big Data on Business and SocietyDigital Transformation in Industry

From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Ahmed, Imran, Jeon, Gwanggil, Piccialli, Francesco·2022·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2022.3146552

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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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Background
The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review
Bibri, S. E., Huang, J., Jagatheesaperumal, S. K., & Krogstie, J. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2024.100433 · Cited by 276 · see citing works
“Ethical and social considerations entail fairness, bias, transparency, and accountability in decision-making processes [8,169,174 e 176], with far-reaching implications.”
Background
Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Ding, R. F., Chen, J., Chen, Y., Liu, J., Bando, Y., & Wang, X. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00844h · Cited by 252 · see citing works
Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0
Ryalat, M., ElMoaqet, H., & Alfaouri, M. (2023) · Applied Sciences · doi:10.3390/app13042156 · Cited by 244 · see citing works
“[36].”
A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, et al. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00350-5 · Cited by 235 · see citing works
“For example, studies have highlighted the different AI techniques and technologies that can be applied in various domains including medicine (He et al., 2019), industry (Ahmed et al., 2022), and energy (Ali & Choi, 2020).”
Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence
Zhao, C. (2022) · Journal of Process Control · doi:10.1016/j.jprocont.2022.06.011 · Cited by 217 · see citing works
AI-enabled organoids: Construction, analysis, and application
Bai, L., Wu, Y., Li, G., Zhang, W., Zhang, H., & Su, J. (2023) · Bioactive Materials · doi:10.1016/j.bioactmat.2023.09.005 · Cited by 216 · see citing works
Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists
Retzlaff, C. O., Angerschmid, A., Saranti, A., Schneeberger, D. M., Röttger, R., Müller, H., & Holzinger, A. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101243 · Cited by 206 · see citing works
“Since understanding complex models is essential in the industry, data scientists are therefore more and more focused on developing models that not only provide accurate predictions, but also offer explanations for their decisions (Ahmed et al., 2022).”
Explainable artificial intelligence (XAI) for intrusion detection and mitigation in intelligent connected vehicles: a review
Nwakanma, C. I., Ahakonye, L. A. C., Njoku, J. N., Odirichukwu, J. C., Okolie, S. A., Uzondu, C., Ndubuisi Nweke, C. C., & Kim, D.-S. (2023) · Applied Sciences · doi:10.3390/app13031252 · Cited by 202 · see citing works
Explainable artificial intelligence in Alzheimer’s disease classification: a systematic review
Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10192-x · Cited by 195 · see citing works
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Masked Swin Transformer Unet for Industrial Anomaly Detection
Jiang, J., Zhu, J., Bilal, M., Cui, Y., Kumar, N., Dou, R., Su, F., & Xu, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3199228 · Cited by 196 · see citing works
“THE purpose of industrial anomaly detection is to detect image areas that deviate from a normal appearance using various artificial intelligence based methods [1], [2].”
Background
A Survey of Explainable Artificial Intelligence for Smart Cities
Javed, A. R., Ahmed, W., Pandya, S., Maddikunta, P. K. R., Alazab, M., & Gadekallu, T. R. (2023) · Electronics · doi:10.3390/electronics12041020 · Cited by 208 · see citing works
Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects
Sarker, I. H., Janicke, H., Mohsin, A., Gill, A. Q., & Maglaras, L. A. (2024) · ICT Express · doi:10.1016/j.icte.2024.05.007 · Cited by 227 · see citing works
Advancing Education Through Extended Reality and Internet of Everything Enabled Metaverses: Applications, Challenges, and Open Issues
Jagatheesaperumal, S. K., Ahmad, K., Al‐Fuqaha, A., & Qadir, J. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3358859 · Cited by 229 · see citing works
Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
Jagatheesaperumal, S. K., Pham, Q.-V., Ruby, R., Yang, Z., Xu, C., & Zhang, Z. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3215676 · Cited by 119 · see citing works
“[46] XAI and Industry 4.0 A summary of the XAI models are used in Industry 4.0 applications.”
Influential
Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey
Kök, İ., Okay, F. Y., Muyanlı, Ö., & Özdemi̇r, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3287678 · Cited by 176 · see citing works
“Here, considering the goals of XAI, the following additional reasons motivate us to adopt XAI approaches in the ﬁeld of IoT [14], [15], [16], as shown in Fig.”
Understanding Human-Centred AI: a review of its defining elements and a research agenda
Schmager, S., Pappas, I. O., & Vassilakopoulou, P. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2448719 · Cited by 191 · see citing works
AI methods in materials design, discovery and manufacturing: A review
Papadimitriou, I., Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, I. (2024) · Computational materials science · doi:10.1016/j.commatsci.2024.112793 · Cited by 133 · see citing works
Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey
Moosavi, S., Farajzadeh-Zanjani, M., Razavi‐Far, R., Palade, V., & Saif, M. (2024) · Electronics · doi:10.3390/electronics13173497 · Cited by 153 · see citing works
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124 · see citing works
“XAI reviews dedicated to other areas such as genetics [49], anomaly detection [50], automotive industry [51], [52], automation and smart industry [53], materials science [54], and language processing [55] can also be found, proving the interdisciplinary relevance of XAI research.”
BackgroundMethod
A Survey on Intelligent Network Operations and Performance Optimization Based on Large Language Models
Long, S., Tan, J., Mao, B., Tang, F., Li, Y., Zhao, M., & Kato, N. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3526606 · Cited by 127 · see citing works
“The Application Prospect of New Generation Intelligent Network for 6G Technology W ITH the rapid development of artiﬁcial intelligence (AI), machine learning, and other technologies, intelligence and automation have become signiﬁcant trends across various industries [1], [2], [3], [4].”
Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects
Shadi, M. R., Mirshekali, H., & Shaker, H. R. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115668 · Cited by 127 · see citing works
Artificial intelligence tools expand scientists' impact but contract science's focus
Hao, Q., Xu, F., Li, Y., & Evans, J. (2026) · Nature · doi:10.1038/s41586-025-09922-y · Cited by 117 · see citing works
“Artificial intelligence (AI) has made considerable strides in recent decades, promising to affect myriad aspects of society, including education 12,13 , healthcare 14,15 and industry 16 .”
The artificial intelligence technologies in Industry 4.0: A taxonomy, approaches, and future directions
Alenizi, F. A., Abbasi, S., Mohammed, A. H., & Rahmani, A. M. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109662 · Cited by 105 · see citing works
Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications
Pati, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3585609 · Cited by 100 · see citing works
“To mitigate these challenges, researchers are integrating explainable AI (transparent and understandable AI decisions) - see [118], [119], interpretable cognitive architectures (human-like, transparent reasoning frameworks) -see [120], Neurosymbolic AI (which combines neural and symbolic reasoning)…”
A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches
Kosasih, E. E., Papadakis, E., Baryannis, G., & Brintrup, A. M. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2281663 · Cited by 118 · see citing works
Deep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems With Reconfigurable Machine Tools
Huang, J., Huang, S., Moghaddam, S. K., Lu, Y., Wang, G., Yan, Y., & Shi, X. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3431095 · Cited by 102 · see citing works
“…technology (ICT) and artiﬁcial intelligence (AI), requires a new paradigm of manufacturing systems that can respond in real time to meet the changing demands and conditions in factories and supply networks and satisfy varying customer needs, that is, smart manufacturing systems [1], [2], [3].”
Background
From anxiety to action: exploring the impact of artificial intelligence anxiety and artificial intelligence self-efficacy on motivated learning of undergraduate students
Chen, C., Hu, W., & Wei, X. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2024.2440877 · Cited by 116 · see citing works
“Indeed, AI has significantly impacted fields such as science (Ahmed & Piccialli, 2022), education (Zhou, 2023), medicine (Zhang et al., 2024), technology and business (Ma & Ye, 2022).”
Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions
Trivedi, C., Bhattacharya, P., Prasad, V. K., Patel, V., Singh, A., Tanwar, S., Sharma, R., Aluvala, S., Pau, G., & Sharma, G. (2024) · IEEE Open Journal of Industry Applications · doi:10.1109/ojia.2024.3399057 · Cited by 106 · see citing works
“In 2022, Ahmad et al. [62] examined AI-and EXAI-based methods used in the context of Industry 4.0.”
MethodInfluential
Transparent Operator Network: A Fully Interpretable Network Incorporating Learnable Wavelet Operator for Intelligent Fault Diagnosis
Li, Q., Li, H., Hu, W., Sun, S., Qin, Z., & Chu, F. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3366993 · Cited by 93 · see citing works
“To recognize health states, the IFD leverages the widespread adoption of Artiﬁcial Intelligence (AI), which facilitates autonomous execution of tasks, including self-monitoring, diagnosis, and analysis [2], [3].”
Method
Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples
Liu, Y., Jiang, H., Yao, R., & Zeng, T. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111507 · Cited by 93 · see citing works
Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
Perkins, M., & Roe, J. (2023) · F1000Research · doi:10.12688/f1000research.142411.2 · Cited by 111 · see citing works
Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements
Alzubaidi, L., Al-Sabaawi, A., Bai, J., Dukhan, A., Alkenani, A. H., Al‐Asadi, A., Alwzwazy, H. A., Manoufali, M., Fadhel, M. A., Albahri, A. S., et al. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/4459198 · Cited by 106 · see citing works
A review of explainable artificial intelligence in smart manufacturing
Madathil, A. P., Luo, X., Liu, Q., Walker, C. T., Madarkar, R., & Qin, Y. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2513574 · Cited by 111 · see citing works
“Smart manufacturing faces complex challenges, and XAI plays a vital role in addressing these challenges by making AI decisions understandable to human operators and decision-makers (Ahmed, Jeon, and Piccialli 2022).”
Artificial intelligence and industrial applications-A revolution in modern industries
Malik, S., Muhammad, K., & Waheed, Y. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.102886 · Cited by 91 · see citing works
Cobotics: The Evolving Roles and Prospects of Next‐Generation Collaborative Robots in Industry 5.0
Rahman, M. M., Khatun, F., Jahan, I., Devnath, R., & Bhuiyan, M. A.-A. (2024) · Journal of Robotics · doi:10.1155/2024/2918089 · Cited by 90 · see citing works
Sources of Risk of AI Systems
Steimers, A., & Schneider, M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19063641 · Cited by 97 · see citing works
Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications
Kausik, A. K., Rashid, A. B., Baki, R., & Maktum, M. M. J. (2025) · Array · doi:10.1016/j.array.2025.100393 · Cited by 90 · see citing works
Explainable artificial intelligence in finance: A bibliometric review
Chen, X.-Q., Ma, C., Ren, Y., Lei, Y.-T., Huynh, N. Q. A., & Narayan, S. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104145 · Cited by 88 · see citing works
AI and Digital Transformation in Higher Education: Vision and Approach of a Specific University in Vietnam
Khanh, Q. V., Thanh, B. T., Chehri, A., Linh, D. M., & Tuan, D. A. (2023) · Sustainability · doi:10.3390/su151411093 · Cited by 139 · see citing works
A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions
Theodorakopoulos, L., Theodoropoulou, A., & Stamatiou, Y. C. (2024) · Eng—Advances in Engineering · doi:10.3390/eng5030068 · Cited by 101 · see citing works
Digital twin-inspired methods for rotating machinery intelligent fault diagnosis and remaining useful life prediction: A state-of-the-art review and future challenges
Ma, H., Fan, C., Zhang, Y., Wang, Q., Yu, K., & Ma, Z. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112770 · Cited by 92 · see citing works
Explainable AI for Medical Image Analysis in Medical Cyber-Physical Systems: Enhancing Transparency and Trustworthiness of IoMT
Liu, W., Zhao, F., Shankar, A., Maple, C. R., Peter, J. D., Kim, B., Słowik, A., Parameshachari, B. D., & Lv, J. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3336721 · Cited by 82 · see citing works
“Intrinsic explainability means that the machine learning model is explainable, while post-explainability is the use of explainability to explain complex machine learning models [16], [17].”
Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions
Ofosu-Ampong, K. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100127 · Cited by 81 · see citing works
Explainable AI for Cyber-Physical Systems: Issues and Challenges
Hoenig, A., Roy, K., Acquaah, Y. T., Yi, S., & Desai, S. S. (2024) · IEEE Access · doi:10.1109/access.2024.3395444 · Cited by 81 · see citing works
“Some AI methods currently utilized in manufacturing and industry, typically ML-or DL-based, are used to reduce downtime by predicting required maintenance [13].”
BackgroundMethodInfluential
Multiscale Channel Attention-Driven Graph Dynamic Fusion Learning Method for Robust Fault Diagnosis
Zhang, X., Liu, J., Zhang, X., & Lu, Y. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3397401 · Cited by 81 · see citing works
“Therefore, it is important to study and develop the technology of mechanical fault detection and diagnosis [1], [2].”
Background
XAIR: A Framework of Explainable AI in Augmented Reality
Xu, X., Yu, A., Jonker, T. R., Todi, K., Lu, F., Qian, X., Belo, J. M. E., Wang, T., Li, M., Mun, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581500 · Cited by 84 · see citing works
“Recent work has started to explore the application of XAI in AR [16].”
Background
Intelligent robotic systems in Industry 4.0: A review
Soori, M., Dastres, R., Behrooz Arezoo, B., & Jough, F. K. G. (2024) · Journal of Advanced Manufacturing Science and Technology · doi:10.51393/j.jamst.2024007 · Cited by 80 · see citing works
A Survey of Industrial AIoT: Opportunities, Challenges, and Directions
Awaisi, K. S., Ye, Q., & Sampalli, S. (2024) · IEEE Access · doi:10.1109/access.2024.3426279 · Cited by 88 · see citing works
A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion
Rutland, H., You, J., Liu, H., Bull, L., & Reynolds, D. M. (2023) · Bioengineering · doi:10.3390/bioengineering10121410 · Cited by 72 · see citing works
“AI-based systems can be used to automatically recognize patterns in data and assist subject matter experts in their evaluations, particularly in situations where complex knowledge and strategies are involved [42].”
Background
Model-agnostic explainable artificial intelligence methods in finance: a systematic review, recent developments, limitations, challenges and future directions
Khan, F. S., Mazhar, S. S., Mazhar, K., AlSaleh, D., & Mazhar, A. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11215-9 · Cited by 102 · see citing works
A review on effective alarm management systems for industrial process control: Barriers and opportunities
Mustafa, F. E., Ahmed, I., Basit, A., Alvi, U.-E.-H., Malik, S. H., Mahmood, A., & Ali, P. R. (2023) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2023.100599 · Cited by 71 · see citing works
Self-citation
Explainable spatially explicit geospatial artificial intelligence in urban analytics
Liu, P., Zhang, Y., & Biljecki, F. (2023) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083231204689 · Cited by 75 · see citing works
“For example, much of the current XAI development has contributed to the ﬁ eld of computer vision, where convolutional neural networks have played a vital role (Ahmed et al., 2022) while very few efforts have been devoted to GNNs.”
Background
Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant
Yuan, M., Bao, P., Yuan, J., Shen, Y., Chen, Z., Xie, Y., Zhao, J., Li, Q., Chen, Y., Zhang, L., et al. (2024) · Medicine Plus · doi:10.1016/j.medp.2024.100030 · Cited by 82 · see citing works
Shapley-based explainable AI for clustering applications in fault diagnosis and prognosis
Cohen, J., Huan, X., & Ni, J. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02468-2 · Cited by 69 · see citing works
“XAI methods vary by aspects such as explanation scope, model specificity, and the location of the explanations (Ahmed et al. 2022).”
Trust my IDS: An explainable AI integrated deep learning-based transparent threat detection system for industrial networks
Shoukat, S., Gao, T., Javeed, D., Saeed, M. S., & Adil, M. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104191 · Cited by 69 · see citing works
A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation
Mohamed, E., Sirlantzis, K., & Howells, G. (2022) · Displays · doi:10.1016/j.displa.2022.102239 · Cited by 67 · see citing works
“Ahmed et al. [114] explored XAI usage in the industrial revolution (Industry 4.0).”
BackgroundInfluential
Human-in-Loop: A Review of Smart Manufacturing Deployments
Bhattacharya, M., Penica, M., O’Connell, E., Southern, M., & Hayes, M. J. (2023) · Systems · doi:10.3390/systems11010035 · Cited by 69 · see citing works
A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT
Ababio, I. B., Bieniek, J., Rahouti, M., Hayajneh, T. S., Aledhari, M., Verma, D. C., & Chehri, A. (2025) · Future Internet · doi:10.3390/fi17010013 · Cited by 64 · see citing works
“This transparency builds stakeholder trust and ensures compliance with regulatory standards, particularly in high-stakes industrial applications [13].”
Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling
Wang, Y.-C., & Chen, T. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121369 · Cited by 62 · see citing works
Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0
Nikiforidis, K., Kyrtsoglou, A., Vafeiadis, T., Kotsiopoulos, T., Nizamis, A., Ioannidis, D., Votis, K., Tzovaras, D., & Sarigiannidis, P. G. (2024) · ICT Express · doi:10.1016/j.icte.2024.12.001 · Cited by 68 · see citing works
Towards defining industry 5.0 vision with intelligent and softwarized wireless network architectures and services: A survey
Zeb, S., Mahmood, A., Khowaja, S. A., Dev, K., Hassan, S. A., Gidlund, M., & Bellavista, P. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103796 · Cited by 53 · see citing works
Metaverse for Education: Developments, Challenges, and Future Direction
Chamola, V., Peelam, M. S., Mittal, U., Hassija, V., Singh, A., Pareek, R., Mangal, P., Sangwan, D., de Albuquerque, V. H. C., Mahmud, M., et al. (2025) · Computer Applications in Engineering Education · doi:10.1002/cae.70018 · Cited by 77 · see citing works
“These advancements provide opportunities for high ‐ quality educational experiences that prioritize the needs of learners [213].”
Fiber Reinforced Composite Manufacturing With the Aid of Artificial Intelligence – A State-of-the-Art Review
Priyadharshini, M., Balaji, D., Bhuvaneswari, V., Rajeshkumar, L., Rangappa, S. M., & Siengchin, S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09775-y · Cited by 58 · see citing works
Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)
Maarif, M. R., Saleh, A. R., Habibi, M., Fitriyani, N. L., & Syafrudin, M. (2023) · Information · doi:10.3390/info14050265 · Cited by 58 · see citing works
“…by non-experts in this domain [35,36]. interpreting ML models and creating explanations that can be understood by non-experts this domain [35,36]. study aimed to contribute This study aimed to contribute to the ﬁeld of energy usage forecasting by introducing a novel approach that combines…”
BackgroundMethod
AI Trustworthiness in Manufacturing: Challenges, Toolkits, and the Path to Industry 5.0
Ahangar, M. N., Farhat, Z. A., & Sivanathan, A. (2025) · Sensors · doi:10.3390/s25144357 · Cited by 69 · see citing works
Machine learning in design of broadband terahertz absorbers based on composite structures
Ding, Z., Su, W., Luo, Y., Ye, L., Wu, H., & Yao, H. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.112215 · Cited by 57 · see citing works
A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing
Hoffmann, R., & Reich, C. (2023) · Electronics · doi:10.3390/electronics12224572 · Cited by 57 · see citing works
“The aim is to discover patterns that can assist in predicting and eventually anticipating malfunctions [5].”
Influential
A high-precision and transparent step-wise diagnostic framework for hot-rolled strip crown
Ding, C.-Y., Sun, J., Li, X., Peng, W. J., & Zhang, D. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.09.007 · Cited by 55 · see citing works
Explainable AI (XAI): Explained
Reddy, G. P., & Kumar, Y. V. P. (2023) · IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream) · doi:10.1109/estream59056.2023.10134984 · Cited by 54 · see citing works
“However, using such technologies also comes with challenges, especially when they are not transparent or explainable, which can hinder the proper functioning and security of IoT devices and networks [11].”
Background
Industrial cyber-physical systems protection: A methodological review
Canonico, R., & Sperlí, G. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103531 · Cited by 62 · see citing works
Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures
Ahmed, I., Chehri, A., Jeon, G., & Piccialli, F. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3192139 · Cited by 52 · see citing works
“move decisions and investigations which are earlier stood challenging or difficult to achieve [1], [2].”
BackgroundSelf-citation
Blockchain and digital twin empowered trustworthy self-healing for edge-AI enabled industrial Internet of things
Feng, X., Wu, J., Wu, Y., Li, J., & Yang, W. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119169 · Cited by 52 · see citing works
A survey of explainable AI techniques for detection of fake news and hate speech on social media platforms
Gongane, V. U., Munot, M. V., & Anuse, A. D. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00248-9 · Cited by 57 · see citing works
“The applications of AI are manifold covering wide domains like robotics, defense, industrial automation, medical diagnostics, governance, chatbots, banking sector, business, online learning platforms, judicial system, etc. [1].”
Influential
Earliest Possible Global and Local Interpretation of Students’ Performance in Virtual Learning Environment by Leveraging Explainable AI
Adnan, M., Uddin, M. I., Khan, E., Alharithi, F. S., Amin, S., & Alzahrani, A. A. (2022) · IEEE Access · doi:10.1109/access.2022.3227072 · Cited by 57 · see citing works
“crucial to understand the decisions and build trust in XAI systems [24], [25].”
Background
Machine learning for small molecule drug discovery in academia and industry
Volkamer, A., Riniker, S., Nittinger, E., Lanini, J., Grisoni, F., Evertsson, E., Rodríguez-Pérez, R., & Schneider, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100056 · Cited by 48 · see citing works
Industrial process fault diagnosis based on feature enhanced meta-learning toward domain generalization scenarios
Gao, Y., Qi, J., Sun, Y., Hu, X., Dong, Z., & Sun, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111506 · Cited by 47 · see citing works
An explainable artificial intelligence model for predictive maintenance and spare parts optimization
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Influential
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Authors: Ahmed, I., Jeon, G., & Piccialli, F.
Affiliations: Institute of Management Sciences Peshawar (PK)
Incheon National University (KR)
University of Naples Federico II (IT)
ORCID: Ahmed, Imran
Jeon, Gwanggil
Piccialli, Francesco
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-08
Volume: 18
Issue: 8
Pages: 5031-5042
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2022.3146552
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

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