Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

Abstract

The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.