## 5 Discussion and Conclusions
ML-based DT has the potential to identify design schematics and concepts of new products, optimisation, consistency, and design validation. In the service stage of the PLM, ML-based DT can manage real-time data, trigger diagnostic procedures, perform data analytics for fault prognosis, and optimise features. **RQ4:** Issues identified by the scientific community can be categorised into three classes: data analytics, model-based tasks, and data-based tasks. A comparison of future work with current work shows that the scientific community is focused on data analytics, while future work emphasises model-based tasks such as validation and DT-driven PLM. Data-driven future directions are also being emphasised by the scientific community. DT-based ML has good potential for managing data heterogeneity, encapsulating dynamic environments, and working in progress. This trend in industrial applications will continue in the future. In addition, the success of deploying DT in real industrial cases requires quantification. DT is a replica of a physical system. Information about the physical system resides in the DT, which requires extra safety from leakage. Therefore, the cybersecurity and security of communication protocols must be the focus. Additionally, causality, explainability, and semantic ontology will be future trends. The main concluding points are listed below:  
* Based on the bibliometric analysis, it can be concluded that there has been a reciprocal increase in interest in ML-based DT. However, the improvements introduced in ML-based DT are focused primarily on the ML part rather than complete DT architecture or manufacturing processes. A collaborative work between authors with ML and manufacturing backgrounds can create a consolidated ML-based DT for use in manufacturing. * It can also be concluded that ML tasks are becoming more advanced over time in ML-based DT. The sole application of ML in manufacturing is no longer considered as a significant contribution to the state of the art. However, the advancement in ML tasks needs quantification and comparison with other domains such as healthcare. * Additionally, it can be concluded that ML acts as the main player in cyberphysical system intelligence enhanced by ML-based DT. ML has potential in enhancing each dimension of DT. In the future, industrial application and encapsulation of dynamic processes will be focused on primarily. **Author Contributions:** S.S.S. collected the publications, analysed them and wrote the proposed review work. M.U.A. and S.B. helped in creating research questions, styling, and structuring the proposed review work through review and discussion. All authors have read and agreed to the published version of the manuscript. **Funding:** The study was conducted through the project DIGICOGS which is financed by Vinnova (Vinnovas Diarienr: 2019-05322) under the innovation program Process Industrial IT and Automation (PiiA). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the findings of this study area available in: [[https://doi.org/10.5281/zenodo.6542836](https://doi.org/10.5281/zenodo.6542836)]([https://doi.org/10.5281/zenodo.6542836](https://doi.org/10.5281/zenodo.6542836)). These data were derived from the following resources available in the public domain: Malardalen University library, accessed on 15 March 2022. **Conflicts of Interest:** The authors declare no conflict of interest. **Definitions:** The ML based DT is explored in the proposed study to create a consolidated knowledge and future path.