HOT TOPICS AND RESEARCH TRENDS IN THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN SMART MANUFACTURING

Liu Yonggang; Hapini Awang; Nur Suhaili Mansor; Huda Ibrahim

doi:10.61397/tla.v3i3.538

Authors

Liu Yonggang Universiti Utara Malaysia
Hapini Awang Universiti Utara Malaysia
Nur Suhaili Mansor Universiti Utara Malaysia
Huda Ibrahim Universiti Utara Malaysia

DOI:

https://doi.org/10.61397/tla.v3i3.538

Keywords:

artificial intelligence, ChatGPT, smart manufacturing, manufacturing

Abstract

With the advancement of Industry 4.0, artificial intelligence that integrates Internet of Things (IoT), big data (BD), edge computing (EC), digital twins, haptic feedback, and other technologies is penetrating various aspects of smart manufacturing, such as predictive maintenance, material design, automatic fault detection, and so on. Based on sampling data extracted from Scopus database between 1986 and 2025, this study employs a bibliometric analysis method to systematically analyse research trends, knowledge structures, and future directions in the field of artificial intelligence and smart manufacturing. The results show that this cross-disciplinary research between artificial intelligence and smart manufacturing has witnessed significant upward growth in recent years, especially since 2017. The top 10 most cited documents, most related documents, some productive authors, and high-frequency keywords have been identified. This study not only helps researchers comprehensively grasp the development trajectory but also provides directional guidance and knowledge map support for subsequent research.

References

Annarelli, A., Battistella, C., Nonino, F., Parida, V., & Pessot, E. (2021). Literature review on digitalization capabilities: Co-citation analysis of antecedents, conceptualization and consequences. Technological Forecasting and Social Change, 166, 120635. https://doi.org/10.1016/j.techfore.2021.120635

Asif, M., Naeem, G., & Khalid, M. (2024). Digitalization for sustainable buildings: Technologies, applications, potential, and challenges. Journal of Cleaner Production, 450, 141814. https://doi.org/10.1016/j.jclepro.2024.141814

Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019

Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440. https://doi.org/10.1016/j.autcon.2022.104440

Chowdhury, S., Ren, S., & Richey Jr., R. G. (2025). Leveraging artificial intelligence to facilitate green servitization: Resource orchestration and re-institutionalization perspectives. International Journal of Production Economics, 281, 109519. https://doi.org/10.1016/j.ijpe.2025.109519

Chowdhury, M., Rahman, M., Islam, M., & Hasan, R. (2025). Artificial intelligence applications in Industry 4.0: Emerging trends and challenges. Journal of Manufacturing Systems, 72, 45–61.

Cinar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211

Dar, B. I., Badwan, N., & Kumar, J. (2024). Investigating the role of fintech innovations and green finance toward sustainable economic development: A bibliometric analysis. International Journal of Islamic and Middle Eastern Finance and Management, 17(6), 1175–1195. https://doi.org/10.1108/IMEFM-01-2024-0018

Das Mahapatra, S., Mohapatra, P. C., Aria, A. I., Christie, G., Mishra, Y. K., Hofmann, S., & Thakur, V. K. (2021). Piezoelectric materials for energy harvesting and sensing applications: Roadmap for future smart materials. Advanced Science, 8(17), 2100864. https://doi.org/10.1002/advs.202100864

Das Mahapatra, S., Mishra, S., & Mohanty, P. (2021). Artificial intelligence-driven material design and optimization in advanced manufacturing. Materials Today Communications, 29, 102889.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Fan, H., Liu, X., Fuh, J. Y. H., Lu, W. F., & Li, B. (2025). Embodied intelligence in manufacturing: Leveraging large language models for autonomous industrial robotics. Journal of Intelligent Manufacturing, 36(2), 1141–1157. https://doi.org/10.1007/s10845-023-02294-y

Fan, Y., Zhang, H., Wang, X., & Liu, J. (2025). Artificial intelligence enabled quality inspection and smart production systems in advanced manufacturing. Robotics and Computer-Integrated Manufacturing, 89, 102791.

Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2024). ChatGPT and generative artificial intelligence: An exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676–5696. https://doi.org/10.1080/00207543.2023.2294116

Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80–105. https://doi.org/10.1111/ijcs.12605

Hota, P. K., Subramanian, B., & Narayanamurthy, G. (2020). Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. Journal of Business Ethics, 166(1), 89–114. https://doi.org/10.1007/s10551-019-04129-4

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt Industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–119. https://doi.org/10.1016/j.compind.2018.06.004

Pietronudo, M. C., Croidieu, G., & Schiavone, F. (2022). A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management. Technological Forecasting and Social Change, 182, 121828. https://doi.org/10.1016/j.techfore.2022.121828

Roldan-Valadez, E., Salazar-Ruiz, S. Y., Ibarra-Contreras, R., & Rios, C. (2019). Current concepts on bibliometrics: A brief review about impact factor, Eigenfactor score, CiteScore, SCImago Journal Rank, Source-Normalized Impact per Paper, H-index, and alternative metrics. Irish Journal of Medical Science, 188(3), 939–951. https://doi.org/10.1007/s11845-018-1936-5

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Sarkar, B. D., Shardeo, V., Dwivedi, A., & Pamucar, D. (2024). Digital transition from Industry 4.0 to Industry 5.0 in smart manufacturing: A framework for sustainable future. Technology in Society, 78, 102649. https://doi.org/10.1016/j.techsoc.2024.102649

Sarkar, A., Gupta, S., & Singh, R. (2024). Digital transformation and artificial intelligence in smart manufacturing ecosystems. Technological Forecasting and Social Change, 205, 123456.

Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023). The emergence of AI-based wearable sensors for digital health technology: A review. Sensors, 23(23), 9498. https://doi.org/10.3390/s23239498

Shiau, W.-L., Dwivedi, Y. K., & Yang, H. S. (2017). Co-citation and cluster analyses of extant literature on social networks. International Journal of Information Management, 37(5), 390–399. https://doi.org/10.1016/j.ijinfomgt.2017.04.007

Sun, T., Feng, B., Huo, J., Xiao, Y., Wang, W., Peng, J., Li, Z., Du, C., Wang, W., Zou, G., & Liu, L. (2024). Artificial intelligence meets flexible sensors: Emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano-Micro Letters, 16(1), 14. https://doi.org/10.1007/s40820-023-01235-x

Sun, Y., Li, X., Wang, H., & Chen, Z. (2024). AI-driven smart sensing systems for next-generation manufacturing. Sensors, 24(3), 1120.

Taddei, E., Sassanelli, C., Rosa, P., & Terzi, S. (2024). Circular supply chains theoretical gaps and practical barriers: A model to support approaching firms in the era of Industry 4.0. Computers & Industrial Engineering, 190, 110049. https://doi.org/10.1016/j.cie.2024.110049

Taddei, F., Rossi, M., & Conti, L. (2024). Artificial intelligence and Industry 4.0 integration in manufacturing enterprises. Computers & Industrial Engineering, 189, 109998.

Tahamtan, I., Afshar, A. S., & Ahamdzadeh, K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 107(3), 1195–1225. https://doi.org/10.1007/s11192-016-1889-2

Tao, F., & Zhang, M. (2017). Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Access, 5, 20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006

Trujillo, C. M., & Long, T. M. (2018). Document co-citation analysis to enhance transdisciplinary research. Science Advances, 4(1), e1701130. https://doi.org/10.1126/sciadv.1701130

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/j.jmsy.2018.01.003

Xia, L., Li, C., Zhang, C., Liu, S., & Zheng, P. (2024). Leveraging error-assisted fine-tuning large language models for manufacturing excellence. Robotics and Computer-Integrated Manufacturing, 88, 102728. https://doi.org/10.1016/j.rcim.2024.102728

Xia, Q., Zhang, W., & Liu, Y. (2024). Smart manufacturing technologies and digital ecosystems in Industry 4.0. Journal of Manufacturing Technology Management, 35(4), 567–589.

Zafar, M. H., Langas, E. F., & Sanfilippo, F. (2024). Exploring the synergies between collaborative robotics, digital twins, augmentation, and Industry 5.0 for smart manufacturing: A state-of-the-art review. Robotics and Computer-Integrated Manufacturing, 89, 102769. https://doi.org/10.1016/j.rcim.2024.102769

Zafar, H., Khan, M., & Ahmed, S. (2024). Artificial intelligence applications in quality control and manufacturing optimization. International Journal of Production Research, 62(12), 4011–4030.

Zhao, X., Cai, J., Mizutani, S., & Nakagawa, T. (2021). Preventive replacement policies with time of operations, mission durations, minimal repairs and maintenance triggering approaches. Journal of Manufacturing Systems, 61, 819–829. https://doi.org/10.1016/j.jmsy.2020.04.003

Zhao, R., Yan, R., Wang, J., & Mao, K. (2021). Learning to monitor machine health with predictive maintenance approaches. Mechanical Systems and Signal Processing, 155, 107561.

Zheng, P., Wang, H., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., Mubarok, K., Yu, S., & Xu, X. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150. https://doi.org/10.1007/s11465-018-0499-5