## 4 Research Question Results
### Third Research Question  
In the reviewed publications, nearly 70% of the ML models played a primary role (Figure 15) such as training of the DT model [24; 26; 34; 39; 48; 49], fault prediction [3; 27; 30; 31; 39; 41; 50; 51; 52], process planning [53; 54], and classification [26; 50]. ML performed the task directly mapped to the DT task in a primary role. Conversely, nearly 30% of the publications showed ML in a supportive role, such as data quality enhancement [12; 29], DT as environment [12], approximation and planning [55; 56], and data mining [57]. The eight dimensions of DT described by the CIRP encyclopaedia [16] are listed in Table 2. The ML tasks contributing to the DT dimensions are listed in the Table. The criteria column states the criteria that need to be fulfilled by ML to consider the ML task as a contribution to the DT dimension [16; 58]. Major ML contributions map to CPS intelligence. This contribution ranged from central control, maintenance, and prediction to quality inference in 2020 and 2021. Apart from this digital model, richness, simulation capability, and connectivity mode are the DT dimensions contributed to by the ML. Figure 15: ML’s role in DT. \begin{table}
\begin{tabular}{c c c} \hline
**DT S Dimensions** & **Dimensional Criteria** & **ML Contributing to DT Dimensions** \\ \hline Integration breadth & Interaction of physical world with virtual world in the form of data acquisition and scheduling & **Data acquisition** acquire process knowledge. **Scheduling** schedule manufacturing tasks, identify optimal actions [53] \\ Connectivity mode & Context-aware, bidirectional or unidirectional connection in between physical and virtual word & **Context-aware connection:** fault diagnosis, deep transfer learning (DPOD), real-time monitoring, and predictive maintenance. [50] [online controlling instruction [34], optimal process plan. **Bidirectional connection** real-time synchronous [54] \\ Update frequency & Event-driven, hourly, daily or monthly update of digital model & **Event-driven update** real-time controlling instruction [34] \\  & & **AI** rule mining, data fusion [12], fault prediction [5], predictive energy efficiency [37], predictive maintenance, feature extraction [50], computing data error on [51], time prediction [51], prediction [50], resource performance prediction [51], cutting tool were perfect [51], prediction [51], prediction [52], and automatic \\ CFS intelligence & Cyberphysical System enhanced by AI, cognition, and automation & product quality inference, accuracy evaluation [59], yet defect analysis [51], optimal process plan [54], improved decision [50], open-parallel pollution, providing cognitive abilities [51], production quality classification [52], real-time monitoring [51], validation [52], prediction [53], real-time monitoring [54], verification [55], prediction control and resource maintenance [56], classification [57], behavior analysis [54], adaptively control manipulated variables [54], data analytics [56], local wear analysis [54], optimized process plans and workflows [54], and visualization [59]. \\ Simulation capabilities & Simulation of physical process on ad hoc or continuous basis & precess simulation [63], automated simulation model generation, [64] \\ Digital model richness & Robustness, resilience, self-adaption, fidelity of virtual model & Robustness, resilience, self-adaption, fidelity [51], fidelity [51], DTL behaviour model [57], high-fidelity of DTs [54] \\ Human interaction & Bridging human and machine & Human-machine collaboration [5], bridges a human user and robot [55] \\ Product life-cycle & Product design, manufacturing and service & **Service stage** service, data analytics [38], **Full** product life-cycle management [37],(3), **Massufacturing stage** fault prediction [5], predicting energy efficiency [37], predictive maintenance, feature extraction [50] \\ \hline \end{tabular}
\end{table} 
Table 2: ML contributing to DT dimensions. Figure 16 shows contribution of ML-based DT in manufacturing PLM.