Mapping the Research Landscape and Predicting Future Growth: A Bibliometric and Time Series Study of Artificial Intelligence/Machine Learning Applications in Mechatronics Engineering Using Web of Science Data

Yıl 2025, Cilt: 18 Sayı: 2, 659 - 678, 31.08.2025

Tülbiya Karahan , Mustafa Çakır , Mesut Yılmaz , Okan Oral

https://doi.org/10.18185/erzifbed.1696490

Öz

This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on the fields of Mechatronics Engineering, Robotics, and Automation. Through a comprehensive bibliometric analysis of publications indexed in the Web of Science (WoS) database, the historical development, key research trends, and prominent themes of this interdisciplinary domain are revealed. Additionally, six different time series forecasting methods—ARIMA, ETS, Theta, Holt-Winters, Polynomial Regression, and Naive Model—are employed to predict the number of scientific publications for the year 2025. The analysis results indicate a growing influence of AI/ML in the field of mechatronics and a clear upward trend in publication volume. This study offers a unique perspective on the research directions in the field by quantitatively illustrating how AI and ML interact with mechatronics engineering.

Anahtar Kelimeler

Mechatronics , Artificial Intelligence Applications , Bibliometric Analysis , Time Series Analysis , Publication Trends

Mekatronik Mühendisliğinde Yapay Zekâ ve Makine Öğrenmesi Uygulamalarında Web of Science Tabanlı Bibliyometrik ve Zaman Serisi Analizi: Araştırma Eğilimlerinin Belirlenmesi ve Gelecek Yayın Sayısının Tahmini

Öz

Bu çalışma, Yapay Zekâ (YZ) ve Makine Öğrenmesi (ML) teknolojilerinin Mekatronik Mühendisliği, Robotik ve Otomasyon alanlarındaki etkisini incelemektedir. Web of Science (WoS) veri tabanındaki yayınlar üzerinden yapılan kapsamlı bir bibliyometrik analiz ile bu interdisipliner alanın tarihsel gelişimi, temel araştırma eğilimleri ve öne çıkan temaları ortaya konmuştur. Ayrıca, ARIMA, ETS, Theta, Holt-Winters, Polynomial Regression ve Naive Model gibi altı farklı zaman serisi tahmin yöntemi kullanılarak 2025 yılına ait bilimsel yayın sayıları öngörülmüştür. Analiz sonuçları, YZ/ML’nin mekatronik alanındaki yükselen etkisini ve bu alanlarda belirgin bir yayın artış trendini göstermektedir. Bu çalışma, YZ ve ML'nin mekatronik mühendisliğiyle nasıl etkileşim içinde olduğunu nicel verilerle destekleyerek alandaki araştırma yönelimlerine dair farklı bakış açısı sunmaktadır.

Anahtar Kelimeler

Mekatronik , Yapay Zekâ Uygulamaları , Bibliyometrik Analiz , Zaman Serisi Analizi , Yayın Eğilimleri

Kaynakça

• [1] Benko, A., & Lányi, C. S. (2009). History of artificial intelligence. In Encyclopedia of Information Science and Technology, Second Edition (pp. 1759-1762). IGI global.
• [2] Moor, J. (2006). The Dartmouth College artificial intelligence conference: The next fifty years. AI magazine, 27(4), 87-87.
• [3] Gugerty, L. (2006, October). Newell and Simon's logic theorist: Historical background and impact on cognitive modeling. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 50, No. 9, pp. 880-884). Sage CA: Los Angeles, CA: SAGE Publications.
• [4] Dixit, U. S., Hazarika, M., Davim, J. P., Dixit, U. S., Hazarika, M., & Davim, J. P. (2017). History of mechatronics. A brief history of mechanical engineering, 147-164.
• [5] Latin, R. V. (1984) Mechatronics: Developments in Japan and Europe: Proceedings of a TECHNOVA Seminar. Edited by M. McLean, Francis Pinter, London (1983), 129 pp.£ 16.50 (hardback).
• [6] Ahmad, H. (2023) A bibliometric exploration of automation and its impact on mechanical engineering research. Babylonian Journal of Mechanical Engineering, 2023, 38-46.
• [7] Cintra Faria, A. C., & Barbalho, S. C. M. (2023). Mechatronics: a study on its scientific constitution and association with innovative products. Applied System Innovation, 6(4), 72.
• [8] Xiaoyu, L. (2020, December). Application and research of artificial intelligence in mechatronic engineering. In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (pp. 235-238). IEEE.
• [9] Xiang, Y. (2021, May). Exploration of the application of artificial intelligence technology in mechatronics technology based on. In Journal of Physics: Conference Series (Vol. 1915, No. 2, p. 022059). IOP Publishing.
• [10] Gehlot, V., & Rana, P. S. (2024) AI in Mechatronics. Computational Intelligent Techniques in Mechatronics, 1-39.
• [11] Kumar, P., Saha, S. K., Sharma, K., & Jha, A. (2024) A Bibliometric Analysis of Industry 4.0. Advances in Communication, Devices and Networking: Proceedings of ICCDN 2024, Volume 1, 337.
• [12] Rus, D., & Tolley, M. T. (2015) Design, fabrication and control of soft robots. Nature, 521(7553), 467-475.
• [13] Kaduk, S., Shulha, A., Svoboda, I., Varyvoda, I., & Mykytyn, Y. (2023) The use of automated information systems in the investigation of criminal offences. Amazonia Investiga, 12(61), 307–316.
• [14] Du, X., Cui, H., Xu, T., Huang, C., Wang, Y., Zhao, Q., Xu, Y., Wu, X. (2020) Reconfiguration, camouflage, and color‐shifting for bioinspired adaptive hydrogel‐based millirobots. Advanced Functional Materials, 30(10), 1909202.
• [15] Khan, I., & Gunwant, D. F. (2025) “Revealing the future”: an ARIMA model analysis for predicting remittance inflows. Journal of Business and Socio-economic Development, 5(2), 155-170.
• [16] Jain, G., & Mallick, B. (2017) A study of time series models ARIMA and ETS. Available at SSRN 2898968.
• [17] Marrakchi, N., Bergam, A., Fakhouri, H., & Kenza, K. (2023) A hybrid model for predicting air quality combining Holt–Winters and Deep Learning Approaches: A novel method to identify ozone concentration peaks. Mathematical Modeling and Computing, 10(4), 1154-1163.
• [18] Ledolter, J. (2008) Smoothing time series with local polynomial regression on time. Communications in Statistics Theory and Methods, 37(6), 959-971.
• [19] Mononen, T., & Myllymäki, P. (2007) Fast NML computation for naive Bayes models. In International Conference on Discovery Science (pp. 151-160). Berlin, Heidelberg: Springer Berlin Heidelberg.
• [20] Mattera, R., Scepi, G., & Kaur, P. (2025) Forecasting human development with an improved Theta method based on forecast combination. Annals of Operations Research, 1-20. [21] Osorio, N. L., & Osorio, G. E. (2020) An analysis of technical information for mechatronics research. Collection and Curation, 39(4), 117-129. [22] Lincaru, C., Badea, F., Pîrciog, S., Grigorescu, A., Badea, S. I., & Badea, C. R. (2023, August). An overview about mechanics developments and achievements in the context of industry 4.0. In International Conference on Reliable Systems Engineering (pp. 17-41). Cham: Springer Nature Switzerland.