# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 4 Challenges
It is expected that the communication between the physical and virtual world will be much slower and the computational requirements will be multiples of those mentioned in the above literature. It has been reported that the synchronization and the convergence of the physical and the virtual layer fail to create a detailed simulation model for ultra-high visual analytics of the DT model [48; 181]. There are various challenges regarding the use of data analytics and IoT in the context of Industry 4.0 that need to be addressed first before moving on to a photorealistic, fully functional DT of real-time entities and processes [182]. One issue with the IoT and CPS is the compatibility of sensors to integrate old robots or machines. This may lead to the need for new equipment to realize the objective of DT, thereby increasing the cost of the process. As various sensors and modules interact, there is also a concern within the industry regarding the security of networks to prevent cyber-attacks [183; 184], leading to another challenge with DT, which is about ensuring the secure real-time interaction between various modules during the exchange of data [173]. Fifth is the accuracy and the precision of the DT simulation model. Currently, most of the DT models create a virtual copy of robots by capturing their motion in the real world and replicating the same action in the virtual world. DT offers the ability to implement various algorithms that can run in parallel without affecting the real-world setup to find the best parameters for optimal results. However, most of these fail to consider the modelling of various complex processes within a DT model, such as wear and tear of the gripper fingers, servo gears, effect of latency between the gripper and the controller and repeatability of the robot over time. An example illustrated in Figure 12 shows a robot involved in gripping an unevenly shaped object. An actual DT of this process must show the actuation of the gripper, wear and tear of the gripper fingers over time, how the object is gripped, the effect of deformities on the object due to gripping force and if the object falls while the robot is motion due to less force. An accurate representation of the process, factoring in many unknown variables, is required to create a highly accurate model of the virtual factory environment [173]. Some of these details are less important than others and can be avoided in a DT. However, the level of precision required of the DT compared to the real world is still unclear. For instance, the DT for geometric assurance in welding focuses on a conceptual framework model and the use of DT to minimize the geometric deviation between the virtual and the real component by using real-time data for optimization [162; 185]. However, the optimization of a welding process in real-time is very complicated. It requires high-speed communication and data transfer between the real system and the virtual system. The optimization algorithms should be coupled with Finite Element Method (FEM) software to precisely analyse various parameters, such as ideal temperature, welding sequence and welding positions. In addition, visually representing the outcome of the process as accurately as seen in the real world will be impossible to achieve in the short-term. Moreover, from an industry practitioner perspective, DT is still just used as a simulation model, unlike the full functionality that the DT proposes to offer, with the main objective currently being just to gather data from the physical platforms to control the manufacturing lines, rather than running a virtual simulation in parallel [173]. Similarly, the fidelity of the physics engine used for simulation in the virtual environment is an issue, often termed as \"the reality gap\". While there are many alternative physics engines, all of which are constantly improving, realistic simulation is still a significant challenge.