## 4 Research Question Results
### First Research Question
#### 4.1.1 Topic Cluster  
A conceptual structure of the author keywords is drawn with help of Multiple Correspondence Analysis (MCA) and named as Co-word Analysis through Correspondence Analysis. Co-word analysis is a technique for content analysis of textual data. The result is several clusters created based on certain scientific aspects. Each cluster comprises textual information of scientific topics with similar semantic or conceptual information. MCA is a technique to find relative relationships among qualitative variables. In correspondence analysis, residuals of a variable's value from the expected value indicates its association. A significant positive number indicates a stronger relationship. For example, publication A has author keywords \"Digital Twin\", \"Machine Learning\", and \"manufacturing\". Therefore, a variable named publication-A is created with values: \"Digital Twin\", \"Machine Learning\", and \"manufacturing\". Similarly, each publication in the corpus contributed one variable with respective author keywords as the variable value. These newly created variables are used in MCA. Co-occurrence frequency of these  
Figure 5: Percentage of publications by subject category. Bar chart with each bar representing a subject category. Size of the bar implies percentage of publications included in the category. newly created variable values determine their association. Variable values with higher associations are placed in the same cluster. A natural language processing (NLP) routine in addition to [PERSON]'s stemming algorithm is used to extract words from author keywords. [PERSON]'s stemming algorithm identifies inflected and derived words. In the topic dendrogram, four clusters can be identified. The keywords in cluster 1 have the highest association, whereas keywords in cluster 4 have the least. The four identified clusters are: (a) Cluster 1: Computer-integrated manufacturing, (b) Cluster 2: Industry 4.0, (c) Cluster 3: Smart manufacturing, and (d) Cluster 4: Data models. **Cluster 1: Computer-integrated manufacturing. ** Computer integration into manufacturing (red cluster in Figure 8) has paved the way for sustainable, robust, and economically efficient manufacturing. [PERSON] et al. [12] proposed DT shop floor (DTS) with four components: (a) physical shop floor, (b) virtual shop floor, (c) shop-floor service system, and (d) shop-floor DT data. In a subsequent study by [PERSON] et al. [4], they used DT for product lifecycle management, that is, design, manufacturing, and service. The difference between these two studies is that one [12] focuses on the DT architecture, while the other [4] focuses on DT application scenarios. Figure 6: Top authors with citation. A chart with circle size representing number of articles and circle colour representing citation rate for top authors. Figure 8: Topic dendrogram with four clusters. A dendrogram with four clusters of author keywords. Distance between keywords is directly proportional to co-occurrence rate. Figure 7: Top journal and conference productivity and impact factor. Top journal and conference productivity shown with bar chart and impact factor shown with line. bed fusion (LPBF) and directed energy deposition (DED) by combining simulation, in situ sensor data, and ML. This architecture improves performance. The timeline analysis shows that in 2017, the focus was on DT architecture [12] and application scenarios [4], whereas in 2020, the focus was on improved performance [27]. Certain review papers have been published, such as one [19] by [PERSON] et al., which emphasises the DT definition, characteristics, and application domains. Similar to [PERSON] et al. [19], [PERSON] et al. [18] performed a literature review on DT applications and gaps in the current state of the art. In addition, the authors proposed simulation-based DT to fill the identified gap. Conversely, [PERSON] et al. [1] tried to identify the role of AI/ML, big data in the creation of DT, and challenges associated with future studies.