## 4 Research Question Results  
### First Research Question  
The first research question focuses on bibliometric analysis of the collected publications. Bibliometric analysis was performed using the 'bibliomatrix' package in R. Table 1 describes corpus summary. As shown in the table, there are 71 documents from 47 sources (journals, conferences, etc. ), with an annual growth rate of 20.09%. The documents' average age of 2.11 years indicates that ML-based DT is a relatively new research field. The authors' keywords' frequency distribution is 249, which implies that 249 keywords are frequently used by authors of ML-based DT research. It can be concluded from this that versatile technology has not yet been incorporated into ML-based DT, resulting in a higher number of keywords. Documents with international collaboration comprise 12.68% of the total corpus, while the number of multi-authored publications is 68. This implies that robust collaborative research in this field is ongoing. Figure 3: The process to identify the primary studies. Screening method shown with help of numbers, icons and connecting arrows. Figure 4 depicts citation trends with total paper number within a 7-year period. A linear increase in the number of publications from 2015 to 2019 can be seen. After 2019, there was a sharp increase in publication number. The most publications were published in 2021. This trend is supposed to continue in 2022. Conversely, citations per paper was zero in the year 2015. In 2016, only two publications were published [10,11]. However, these publications received a significant number of citations, with 500 citations per paper. In 2017, the number of citations per paper peaked because of two highly cited publications by [PERSON] et al. [4,12]. After 2017, the average number of citations per paper decreased due to an increase in the number of publications. Figure 5 depicts percentage of publications by subject category. According to the figure, most publications belong to 'Computer Science'. Other popular subject categories are: 'Engineering', 'Operations Research And Management Science', and 'Automation And Control Systems'. \begin{table}
\begin{tabular}{c c} \hline \hline
**Timespan** & **2015-2022** \\ \hline Documents & 71 \\ Sources & 47 \\ Annual Growth Rate \% & 20.09 \\ Document Average Age & 2.11 \\ Average citations per documents & 104.5 \\ Average citations per year per document & 27.56 \\ Keywords Plus (ID) & 61 \\ Author’s Keywords (DE) & 249 \\ Authors & 246 \\ Authors of single-authored documents & 3 \\ Authors of multi-authored documents & 68 \\ Docs with international collab \% & 12.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of corpus. Figure 4: Citation trend from 2015 to 2022. Citation trend and paper production rate change over years shown with two lines. Figure 6 depicts the top 10 authors considering total publications (TP) and total citations per year (TC). According to the figure, [PERSON] is the most productive (six publications) [2; 3; 4; 12; 24; 25] and most cited author (300 average citations). Following [PERSON], [PERSON] [5; 25] is the most productive and cited author. Most authors published three papers in the same year and stopped publishing afterwards. Figure 7 represents the top 10 journals and conferences considering total papers (TP). M2 VIP is an abbreviation of _Proceedings of the 2018 25 th international conference on mechatronics and machine vision in practice_, while ICNSC is an abbreviation of _2018 IEEE 15 TH International conference on networking sensing and control_. Additionally, the Figure shows the impact factors of the journals and conferences. The most productive journal is the _Journal of manufacturing systems_, with seven publications, and it has the highest impact factor 8.63. The _Journal of manufacturing systems_ is followed by _IEEE access_, with six publications and an impact factor of 3.36. Additionally, other high-impact-factor journals are _Robotics and computer-integrated manufacturing_, and _computers in industry_.