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

被引：1

作者：

Nikiforidis, Konstantinos ^{[1
]}

Kyrtsoglou, Alkiviadis ^{[1
]}

Kotsiopoulos, Thanasis ^{[1
,2
]}

Vafeiadis, Thanasis ^{[1
]}

Nizamis, Alexandros ^{[1
]}

Ioannidis, Dimosthenis ^{[1
]}

Votis, Konstantinos ^{[1
]}

Tzovaras, Dimitrios ^{[1
]}

Sarigiannidis, Panagiotis ^{[2
]}

机构：

[1] Informat Technol Inst, Ctr Res & Technol Hellas, Thessaloniki 57001, Greece

[2] Univ Western Macedonia, Dept Elect & Comp Engn, Karamanli & Ligeris St, Kozani 50100, Greece

来源：

ICT EXPRESS | 2025年 / 11卷 / 01期

关键词：

Explainable AI; Industry; 4.0; 5.0; Manufacturing application; CONVOLUTIONAL NEURAL-NETWORK; ARTIFICIAL-INTELLIGENCE; CHALLENGES;

D O I：

10.1016/j.icte.2024.12.001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Explainable Artificial Intelligence (XAI) is crucial for the transition from the fourth to fifth industrial revolution, providing transparency and fostering user confidence in Artificial Intelligence (AI) powered systems. Since 2020, XAI applications demonstrate potential to transform manufacturing. This paper provides an extensive overview of XAI-based applications in Industries 4.0 and 5.0 by highlighting the trends regarding methods used, connecting XAI methods with important parameters and presenting XAI visualization approaches. The survey provides valuable insights for researchers, practitioners and industry leaders as it underscores the potential of XAI in shaping the future of manufacturing by enhancing transparency and user acceptance of AI-powered applications.

引用

页码：135 / 148

页数：14

共 94 条

[1]

IBM, Explainable AI (XAI), (2020)

[2]

Wajid U., Nizamis A., Anaya V., Towards industry 5.0 – a trustworthy AI framework for digital manufacturing with humans in control, (2022)

[3]

Rozanec J.M., Zajec P., Kenda K., Novalija T., Fortuna B., Mladenic D., Soldatos J., Stardom: an architecture for trusted and secure human-centered manufacturing systems, Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, Proceedings, Part IV (199–207), (2021)

[4]

Alvarez-Napagao S., Ashmore B., Barroso M., Barrue C., Beecks C., Berns F., Bosi I., Chala S.A., Ciulli N., Garcia-Gasulla M., Grass A., Ioannidis D., Jakubiak N., Kopke K., Lamsa V., Megias P., Nizamis A., Pastrone C., Rossini R., Sanchez-Marre M., Ziliotti L., knowlEdge project – Concept, methodology and innovations for artificial intelligence in Industry 4.0, pp. 1-7, (2021)

[5]

Kotsiopoulos T., Papakostas G., Vafeiadis T., Dimitriadis V., Nizamis A., Bolzoni A., Bellinati D., Ioannidis D., Votis K., Tzovaras D., Sarigiannidis P., Revolutionizing defect recognition in hard metal industry through AI explainability, human-in-the-loop approaches and cognitive mechanisms, Expert Syst. Appl., (2024)

[6]

Adadi A., Berrada M., Peeking inside the black-box: a survey on explainable artificial intelligence (XAI), IEEE Access, 6, pp. 52138-52160, (2018)

[7]

Carvalho D.V., Pereira E.M., Cardoso J.S., Machine learning interpretability: a survey on methods and metrics, Electronics (Switzerland), 8, 8, pp. 1-34, (2019)

[8]

Deshpande N.M., Gite S.S., A brief bibliometric survey of explainable AI in medical field, Libr. Philos. Pract., pp. 1-28, (2021)

[9]

Tjoa E., Guan C., A survey on explainable artificial intelligence (XAI): towards medical XAI, IEEE Trans. Neural Netw. Learn. Syst., pp. 4793-4813, (2020)

[10]

Lotsch J., Kringel D., Ultsch A., Explainable artificial intelligence (XAI) in biomedicine: making AI decisions trustworthy for physicians and patients, BioMedInformatics, 2, (2021)

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