This article will discuss:
- The definition and application of simulation technology and the digital twin
- The differences between a simulation and a digital twin
- The symbiotic relationship between simulations and the digital twin
The increasing use of emerging technology to simplify complex tasks has proved rewarding across every industry in diverse ways. This includes increased operational efficiency, automating manual tasks, training and validation, as well as data analysis. It is a known fact that the integration of emerging tech has brought on the fourth industrial revolution, in which data analytics and automation are important components. The digital transformation of traditional processes is also another aspect of Industrie 4.0 and, here, simulation and the digital twin play starring roles. But what are these roles?
What is simulation?
In computing, simulations refer to digital models that imitate the operations or processes within a system. Such simulations are used for analyzing the performances of systems and the testing and implementation of new ideas. Engineers and technicians make use of simulations across a variety of industries to test products, systems, processes, and concepts.
In many circles, simulations are run using computer-aided design software applications. But for more advanced simulations with many variables, specialized simulation software is used. Typical examples of how simulations function include their use in finite element analysis and stress analysis. In the real world, these tests involve analyzing the effect of external pressure on metals or products to enhance their design or features.
Other types of simulations include discrete event simulations, stochastic simulations, and deterministic simulations. In these types, the variables used in running the simulation are either known or random. To run simulations, some level of digitization is needed. This process may involve only mathematical concepts or the design of 2D or 3D models representing assets within a process or a product. The simulation is then run by introducing variables into the digital environment or interface.
What is a digital twin?
In its basic form, a digital twin is the digital representation of physical or non-physical processes, systems, or objects. The digital twin also integrates all data produced or associated with the process or system it mirrors. Thus, it enables the transfer of data within its digital ecosystem, mirroring the data transfer that occurs in the real world. The data used in digital twins are generally collected from Internet of Things devices, edge hardware, HMIs, sensors, and other embedded devices. Thus, the captured data represents high-level information that integrates the behavioral pattern of digitized assets in the digital twin.
The real-time digital representation a digital twin provides serves as a world of its own. Within this digital world, all types of simulation can be run. It can also be used as a planning and scheduling tool for training, facility management, and the implementation of new ideas. This highlights the fact that a digital twin is a virtual environment, thus it must consist of either 2D or 3D assets or the data they produce or are expected to produce. In the modeled virtual environment, individuals can do what they choose with few limitations including the running of simulations.
Highlighting the differences between simulations and digital twins
Although the definitions of both concepts already highlight key differences, the use of case studies makes these differences more relatable. In 2019, CKE Holdings Inc., the parent company of Hardee’s and Carl Jr’s was interested in enhancing productivity levels within these facilities. The idea was to make order picking by staff easier and reduce shop floor traffic through better layout designs.
While simulations can be used to analyze the shortest distance between workstations or the effects of more storage facilities within the restaurant, a digital twin can do much more. Using a digital twin, CKE Holdings Inc. was able to recreate digital representations of existing shop floors and run multiple simulations, design, and scheduling ideas to enhance productivity. This resulted in improvements in every aspect of the facility’s operation from staff training, scheduling, and meeting customer demands more efficiently.
This shows that while simulations may help with understanding what may happen when changes are introduced, a digital twin helps with understanding both what is currently happening and what may happen within a process. Some key differences include:
- Real-time simulations – Traditional simulations are run in virtual environments that may be representations of physical environments but do not integrate real-time data. The regular transfer of information between a digital twin and its corresponding physical environment makes real-time simulation possible. This increases the accuracy of predictive analytical models and the management and monitoring policies of enterprises.
- Enhancing product design – Advanced simulations have the capacity to analyze thousands of variables to provide diverse answers, but a digital twin can be used to achieve more. Boeing’s integration of digital-twin technology in aircraft design and production is an example of its capabilities. In this case, a digital twin was used to simulate parts of an aircraft to analyze how diverse materials will fare throughout the life-cycle of the aircraft. With these calculations, Boeing was able to achieve 40% improvement in the quality of certain parts it designed.
- Optimize real-world products and processes – Every Tesla automobile running today has a digital twin that captures the large data sets each car produces. The captured data is used in optimizing design, predictive analytics, enhancing self-driving initiatives, and maintenance. This highlights how a digital twin immediately or directly affects the physical entity it represents unlike the theoretical results simulations provide.
Regardless of the path taken, the digital transformation of assets and processes enhances industrial effort in many ways. This includes refining product design, real-time troubleshooting, and implementing new ideas. To achieve a comprehensive digital transformation of existing or planned entities, systems, and processes, accurate data capture is required. Enter smart edge technology or devices.
The accuracy of a simulation or a digital twin relies heavily on the accuracy of the data used in designing its models. In today’s shop floors, data capture is being made possible by smart edge device and human-machine interfaces and only with these types of data can a digital transformation occur.