**5. Results and Discussion**

This work presents a novel methodology for multi-robot manufacturing cell design and operation, combining digital twin and virtual reality. The proposed framework and the modular architecture permit simulation and real-time monitorization. The fulfillment of the requirements is verified in a digital twin framework based on virtual reality, which permits the immersive visualization of the design and the simulation of the possible modifications to find the optimal solution during the virtual commissioning. Once the automated process is implemented in the real world, it is mirrored and linked to its digital twin in the virtual world, which permits real-time monitoring and continuous training and improvement. Thus, this work implies a theoretical outcome, which is the proposed methodology for robot-based automation, and a practical one, which is the digital twin framework with VR visualization used as testbed environment. Results show that the proposed methodology permits the efficient design and real commissioning of multi-robot manufacturing processes, including human–robot collaborative cells, which implies an intelligent, efficient, and unique work environment with high potential applications for process design, implementation, and control. Moreover, digital twins with VR visualization allow humans the possibility to work in totally safe environments with robots.

Table 1 shows the comparison between simulation tools from robot manufacturers, commercial simulation tools with virtual reality, and the digital twin based on virtual reality in terms of low acquisition costs (labeled as "Low investment" in the table), integration of robots from different manufacturers ("Multi-robot"), orientation to human–robot collaboration ("Human-robot collab."), immersive effect and virtual reality ("Immersive"), environment customization ("Customization"), usability for training ("Training"), and versatility to include new functionalities ("Versatility"). The scale 1–3 represents a relative comparison between the tools, where "1" means the worst, or not supported, and "3" means the best. Many companies cannot purchase a specific simulation software for each type of robot when they are studying the introduction of robots in their manufacturing processes. The proposed methodology based on the digital twin is totally affordable as it only requires the VR system as an additional component, which is a mass consumer product. Although the methodology can be extended to the automation of other manufacturing processes, the disadvantage is that it requires an expert developer for the creation of the customized digital twin model and the immersive virtual environment. However, this fact provides great versatility to add new features and functionalities according to the needs of the company.


**Table 1.** Comparative between simulation tools and the proposed approach.

The proposed approach has been validated in the real commissioning of a representative use case of an assembly manufacturing process, where humans and robots from different manufacturers work collaboratively in classification, assembly, inspection, and delivery of batches of parts. Results show that the presented combination of the digital twin concept with virtual reality permits the design, simulation, training, and real-time monitoring of the manufacturing process. The digital twin of the robotic cell permits an efficient and optimized design, evaluating different options for the layout, the use and the number of robots, and other parameters to find the best solution according to lean automation concepts. All of them are validated in the virtual commissioning before the physical implementation. After the implementation, the cell is mirrored in the digital twin, monitoring productivity and safety for the real commissioning—key issues for industrial leadership.

Figure 13 shows the tests in the virtual and real scenarios. Testers point out that the proposed methodology increases the efficiency as the same tool includes all the necessary steps for the real commissioning of the cell, integrating all types of robots and collaborative applications. The intermediate virtual commissioning includes all the minimum details, and the immersive VR visualization gives a sense of total realism (sense of presence). Moreover, this solution is safe, dynamic, and cost-effective. Potential applications can be found in different industries. Thus, a very likely outcome is extending these results to introduce robots in the manufacturing processes of multiple industries and to increase their efficiency. In this sense, the next steps of this work will focus on more complex manufacturing processes, and extend the capabilities to conduct data analysis.

**Figure 13.** Tests and validation: (**a**) tester in the virtual scenario, and (**b**) tester in the real scenario.

In the current Industry 4.0 revolution, where manufacturing technologies are continuously changing in order to achieve personalized products, in contrast with the traditional serial production, intelligent automation is a core element to increase the productivity and to improve the competitiveness of the industry. Robots are the future of the industry, and thus the design, the commissioning, and the operation of the robotized cells are critical to achieving success. The proposed approach demonstrates that the synergies between Industry 4.0 technologies, such as digital twins, virtual reality, and collaborative robotics, enable working at new levels and parallel environments which have not been accomplished yet. The future of manufacturing requires the interaction between humans and multiple types of robots, and between physical and virtual scenarios. Each manufacturing process will have its digital twin not only for visualizing or controlling, but also for continuous improving.

**Author Contributions:** Conceptualization, L.P.; methodology, L.P.; validation, L.P., S.R.-J., and N.R.; writing—original draft, L.P.; writing—review and editing, L.P., R.U., and D.F.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Gobierno del Principado de Asturias Programa Asturias grant number IDI/2018/00063, Robots 4.0.

**Acknowledgments:** The authors would like to thank Eduardo Diez and Paulino San Miguel for their programming support.

**Conflicts of Interest:** The authors declare no conflict of interest.
