## 5 Discussion and Conclusions  
In the proposed review study, 71 documents were included, ranging from 2015 to March 2022. The screening of 1050 publications from Web of Science, IEEE, Scopus, and ScienceDirect resulted in 71 finally selected publications. The novelty of the proposed study lies in exhaustive research on ML-based DT from bibliometric and evolutionary perspectives. **RQ1:** This research question attempts to accumulate quantitative and qualitative knowledge associated with ML-based DT. Based on the citation trend, it can be concluded that there is a reciprocal increase in interest in ML-based DT. Most publications belong to the subject category of computer science rather than manufacturing. Additionally, a marginal number of authors continued their contribution to ML-based DT over time. Clustering the author keywords resulted in four clusters: (a) computer-integrated manufacturing, (b) Industry 4.0, (c) smart manufacturing, and (d) data models. The analysis of keywords showed that the past trend was to create a common knowledge base of definition, characteristics, and applications of DT. The current trend indicates application-oriented performance improvement instead of complete DT architecture improvement. However, it is necessary to quantify the loss caused by considering only application-oriented improvement instead of complete architecture improvement. The total time span was divided into three periods, (a) Period 1 (2015-2017), (b) Period 2 (2018-2019), and (c) Period 3 (2020-2022), in order to analyse the thematic evolution. Analysing these three periods indicates that the themes ML, IoT, and deep learning are emerging over the trajectory of time, while CPS and virtual reality are well developed themes. The relevance of ML to DT has increased over time. Similarly, big data, data models, and real-time systems have become more relevant to ML-based DT. **RQ2:** The dominant ML algorithm in ML-based DT is the neural network. Neural networks have evolved over time using different activation functions, parameters, and learning techniques. All these changes were introduced to address application-specific requirements, such as increased performance, reduced data size, reduced uncertainty, and increased cognition. In the future, neural networks will evolve to incorporate explainability, causality, expert knowledge, resilience, and so on. ML is predominantly used for data analytics, such as fault prediction in DT. The maturity of the other tasks was marginal. The data-oriented ML task received the lowest focus. In the future, ML can contribute to the improvement of DT data quality through procedure creation, issue monitoring, and requirement improvement. In the case of model-based tasks, ML can be used for DT model problem exploration, creation of DT model architecture, development of DT model test cases, and deployment of DT models in industrial scenarios. In the future, ML can contribute to DT confidence via error-free simulation of the physical system, DT fidelity by instant updates in the model, DT quality assurance by advanced data analytics, and minimised DT carbon footprint via using a less resource-hungry platform. **RQ3:** DT's eight dimensions from the CIRP encyclopaedia were considered to assess the contribution of ML to DT dimensions. The CPS intelligence characteristics of the DT were mainly enhanced by ML. Digital model richness, such as DT fidelity, can be improved by ML through a lifelong-learning ML model. The update frequency of DT can be real-time, daily, monthly, or yearly. However, the ML model can trigger this update process in real-time. In the case of human interaction with DT, ML has potential in virtual and augmented reality, or a hybrid of the two, bridging the gap between machines and humans. The role of ML in developing DT shows that it plays a vital role in DT functionality. This trend is expected to continue in the future. Conversely, ML-based DT participates in the manufacturing stage of PLM in most cases.