# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 3 Review of DT in HRC
### Benefits of DT-HRC
#### 3.5.2 Maintenance  
DT can support companies in machine health monitoring and maintenance activities. DT models involve vast amounts of data that are collected and processed at various stages to create a high-fidelity simulation model. These data include real-time data, historical data, technical data and service data. The data can be processed to have a layer by layer understanding of the operations, and to study the behaviour of machines in the long run. The focus on the monitoring of machine health and subsequent maintenance planning using data or cloud-based systems has increased due to the development of Industry 4.0 [155]. An approach that combines various predictive maintenance methods, such as statistics-based reliability, physical model-driven and data-driven methods can decrease the error in predictive maintenance [156]. Various sets of experiments can be conducted simultaneously on the DT model to gather information to forecast the failure [157]. Likewise, corrective maintenance using simulation-based DT utilizing the data collected during the engineering  
Figure 11: The trade-off between the safety and the velocity of the robot joint in HRC to benefit both the production and safe HRC. DT-HRC can be used to create a model that complies with the ISO guidelines as well as optimize the parameters of the robot in real-time. Various parameters are continuously monitored and measured to meet ISO standards.  
phase through production can potentially help minimize the production loss and machine breakdown time.  
Moreover, the timing of corrective maintenance can be highly uncertain [158]. Therefore, gathering information across different sensors in the DT model and using data mining algorithms can provide more accurate system failure models and support human decision-making during maintenance tasks. For example, the data acquired from various sensors and the benefits of cloud computing can assist the non-destructive testing and maintenance of machines by providing real-time data for inspection instead of conventional intervention methods. Therefore, data fusion and modelling is a core aspect of DT [159]. DT enables manufacturing companies to focus on product diagnostics and root cause analysis for product quality management [160]. A real-time DT that uses a probabilistic model of the physical system can be utilized to diagnose the physical system [161]. This is achieved by monitoring and comparing the DT and the physical twin in real-time. If the physical twin's behaviour deviates from the predicted behaviours modelled in the DT, then the part in the DT that causes this deviation is further analyzed. Therefore, data from the DT can be utilized to record the physical and digital footprint of the product throughout its product lifecycle [133].  
DT involves high fidelity graphical simulation environments that can provide flexibility through the visualization of the assembly of products on the shop floor so that there is a minimum variation between the virtual model and the actual model. This type of DT for geometry assurance has been discussed in the context of smart manufacturing through the process of robotic spot welding [162], which enabled parts to be delivered with higher quality. A framework for using DT for the quality inspection of components in a smart factory scenario to measure the flatness of the component is also possible. The critical technology enabling this is IoT and IoS (Internet of Services) for the creation of DT that acts as a reference model [163]. Emulation software can be used alongside DT to improve the functionality of old machines through reconditioning or retrofitting, a much cheaper process than replacing the old machine [96].