Application of Artificial Intelligence and Machine Learning in Manufacturing Industry : A Bibliometric Study
Keywords:
Machine Learning (ML), Artificial Intelligence (AI), Bibliometric analysis, manufacturing industries, ScopusAbstract
Abstract:
Purpose: The use of machine learning (ML) and artificial intelligence (AI) in the manufacturing industries is covered in-depth in this paper's bibliometric examination of the literature.
Methodology: The authors of this study analyzed a dataset of 3,597 papers published between 2003 and 2023 using bibliometric techniques. Using Vosviewer, they primarily focused on performance analysis and scientific mapping of articles. In order to narrow down the scope of the study, authors performed a keyword search on the official publisher websites and in academic databases like Scopus, ACM etc.
Findings: This study gave a quick overview of AI and ML in the manufacturing industry and the trends in publications over the last twenty years. It revealed that “Artificial Intelligence” was the highly used keyword amongst all the papers, with a total occurrence of 2342 and a total link strength of “19448”. The analysis observed that country “China” has done the most publications in the field and stood at first rank with 594 publications to its credit. Research results also concluded that the National Natural Science Foundation of China has sponsored maximum publications. Additionally, the analysis revealed that highest number of publications, i.e., 2290, are undertaken in engineering domain.
Practical implications: A new type of intelligent manufacturing and sustainable manufacturing will be powered by artificial intelligence and machine learning, or AI/ML. Sustainable manufacturing encompasses all aspects of a sustainable process, from supply chain management to quality control, predictive maintenance, and energy consumption. This study will help scientists and researchers in the field of AI and ML with future research.
Originality/value: AI and ML have been used to make a lot of different things happen, like optimizing processes, making things in factories, and predicting how things will turn out. This analysis would provide a comprehensive overview of the major developments in AI and ML over the years. It is a more comprehensive and thorough process due to the techniques employed.
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References
Akshatha, K. R., & Shreedhara, K. S. (2018). Implementation of machine learning algorithms for crop recommendation using precision agriculture. International Journal of Research in Engineering, Science and Management (IJRESM), 1(6), 58-60.
Badesa, F. J., Morales, R., Garcia-Aracil, N., Sabater, J. M., Casals, A., & Zollo, L. (2014). Auto-adaptive robot-aided therapy using machine learning techniques. Computer methods and programs in biomedicine, 116(2), 123-130.
Balamurugan, E., Flaih, L. R., Yuvaraj, D., Sangeetha, K., Jayanthiladevi, A., & Kumar, T. S. (2019, December). Use case of artificial intelligence in machine learning manufacturing 4.0. In 2019 International conference on computational intelligence and knowledge economy (ICCIKE) (pp. 656-659). IEEE.
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492.
Fahle, S., Prinz, C., & Kuhlenkötter, B. (2020). Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application. Procedia CIRP, 93, 413-418.
Gupta, N. (2017). A literature survey on artificial intelligence. International Journal of Engineering Research & Technology (IJERT), 5(19), 1-5.
Moore, A. (2017). Carnegie Mellon Dean of Computer Science on the future of AI. Forbes.
Nti, I. K., Adekoya, A. F., Weyori, B. A., & Nyarko-Boateng, O. (2022). Applications of artificial intelligence in engineering and manufacturing: A systematic review. Journal of Intelligent Manufacturing, 33(6), 1581-1601.
Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778.
Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379-391.
Strozzi F, Colicchia C, Creazza A, Noè C (2017). Literature review on the ‘Smart Factory’ concept using bibliometric tools. International Journal of Production Research, 55(22), 6572-6591.
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