Natural Language Processing Applications in Smart Manufacturing: A Survey and Perspective

Dac Hieu Nguyen, Dac Phuong Thao Nguyen, Quang Chieu Ta, Thi Diem Doan, Sébastien Thomassey and Kim Duc Tran ()
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Dac Hieu Nguyen: Dong A University, International Chair in DS & XAI, International Research Institute for Artificial Intelligence and Data Science
Dac Phuong Thao Nguyen: Thuyloi University, Department of Artificial Intelligence
Quang Chieu Ta: Thuyloi University, Department of Artificial Intelligence
Thi Diem Doan: Dong A University, International Chair in DS & XAI, International Research Institute for Artificial Intelligence and Data Science
Sébastien Thomassey: Université de Lille, ENSAIT, ULR 2461—GEMTEX—Génie et Matériaux Textiles
Kim Duc Tran: Dong A University, International Chair in DS & XAI, International Research Institute for Artificial Intelligence and Data Science

A chapter in Human-Centered Explainable Anomaly Detection for Smart Manufacturing in Industry 5.0, 2026, pp 123-145 from Springer

Abstract: Abstract Natural Language Processing (NLP) is revolutionizing smart manufacturing, turning piles of unstructured text, such as maintenance logs, incident reports, and operational notes, into clear, useful insights that power Industry 5.0. This chapter dives into how NLP is making manufacturing smarter, more efficient, and centered around people. We walk through the basics, from rule-based systems to today’s cutting-edge models like BERT and GPT, showing how they handle the vast amount of text data in modern manufacturing. Key applications are categorized into a taxonomy, including Human-Machine Interaction, Predictive Maintenance, Intelligent Robotics, Ontology-Driven Information Systems, and Patent Analysis, with real-world case studies illustrating their impact. We also get real about the hurdles, such as avoiding manual work, balancing speed with accuracy, and keeping up with ever-changing data, while pointing out where research can push things forward, and looking at how next-gen tools like Foundation Agents could make manufacturing even more seamless, sustainable, and human-friendly. Wrapping up, we provided two case studies that demonstrate how NLP can pull structured data from maintenance records and spot issues in manufacturing logs, delivering impressive results that save time and money.

Keywords: Natural language processing; Smart manufacturing; Predictive maintenance; Human-machine interaction; Large language models (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-13657-2_7

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