Digital Twin and Artificial Intelligence in Machining: A Bibliometric Analysis

Abstract

The past decade has witnessed an exodus toward smart and lean manufacturing methods. The trend includes integrating intelligent methods into sustainable manufacturing systems purposely to improve the machining efficiency, reduce waste and also optimize productivity. Manufacturing systems have seen transformations from conventional methods, leaning towards smart manufacturing in line with the industrial revolution 4.0. Since the manufacturing process encompasses a wide range of human development capacity, it is essential to analyze its developmental trends, thereby preparing us for future uncertainties. In this work, we have used a Bibliometric analysis technique to study the developmental trends relating to machining, digital twins and artificial intelligence techniques. The review comprises the current activities in relation to the development to this area. The article comprises a Bibliometric analysis of 464 articles that were acquired from the Web of Science database, with a search period until November 2024. The method of obtaining the data includes retrieval from the database, qualitative analysis and interpreting the data via visual representation. The raw data obtained were redrawn using the origin software, and their visual interpretations were represented using the VOSviewer software (VOSviewer_1.6.19). The results obtained indicate that the number of publications related to the searched keywords has remarkably increased since the year 2018, achieving a record maximum of over 80 articles in 2024. This is indicative of its increasing popularity. The analysis of the articles was conducted based on the author countries, journal types, journal names, institutions, article types, major and micro research areas. The findings from the analysis are meant to provide a bibliometric explanation of the developmental trends in machining systems towards achieving the IR 4.0 goals. Additionally, the results would be helpful to researchers and industrialists that intend to achieve optimum and sustainable machining using digital twin technologies.

Keywords

Digital twin / Machining / Bibliometric analysis / Artificial Intelligence / Machine learning

Author Contributions

Conceptualization, D.Y.S. and C.L.; Methodology, Q.L.; Software, B.L.; Validation, Y.Z., C.L. and B.Z.; Investigation, D.L.; Data Curation, W.Z., Z.Z., Y.F., Q.B., X.Y. and L.W.; Writing—Original Draft Preparation, D.Y.S.; Writing—Review & Editing, C.L.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Funding

This study was financially supported by the National Natural Science Foundation of China (Grant Nos. 52305477, 52375447, 52305474, and 52205481), the Major Special Projects of Aero-engine and Gas Turbine, China (Grant No. 2017-VII-0002-0095), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20210407), the Special Fund of Taishan Scholars Project, China (Grant No. tsqn202211179), the Youth Talent Promotion Project in Shandong, China (Grant No. SDAST2021qt12), Qingdao Postdoctoral Researchers Applied Research Project, China (Grant No. QDBSH20230102050), the Support plan for Outstanding Youth Innovation Team in Universities of Shandong Province, China (Grant No. 2023KJ114), and Qingdao Science and Technology Planning Park Cultivation Plan (Grant No. 23-1-5-yqpy-17-qy).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

IR 4.0: Industrial revolution 4.0, IoT: Internet of things, WoS: Web of science, ANOVA: Analysis of variance, DT: Digital twin, CNT: Carbon nanotubes, NASA: National aeronautics and space administration, ANN: Artificial neural network, MRR: Material removal rates, GRU: Gated recurrent unit, AI: Artificial intelligence, MQL: Minimum quantity lubrication, ML: Machine learning, CNC: Computer numerical control, EDM: Electric discharge machining, FL: Fuzzy logic.

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