Managers of industrial enterprises need to monitor their equipment, production lines and operational process to optimize maintenance, time spent and production costs. Currently, several industries already have a certain level of digitization to their processes, which allows them to visualize their operation history and improve decision making.
In addition to checking history data, that is, events that have already occurred in the past, there are cases in which it is necessary to carry out assessments of future scenarios. This type of study allows predictions to be made for probable scenarios and, thus, the evaluation of the consequences of each of them is made so that decisions can have greater assertiveness.
These scenario simulations have been taking place for a long time in factory environments, through specialized software. Different types of simulations can be carried out. Design simulations of parts, components and products, for example, are performed to verify the behavior of these assets in situations of loads, processes or diverse environments.
In addition, process simulations can be carried out, using nominal information from the equipment to verify its performance and forecast production line behavior.
Currently, the large volume of processes history data and equipment also allows the construction of so-called Digital Twins. This technique allows the creation of many scenario simulations, with specific applications and great advantages when compared to theoretical design simulations.
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What is Digital Twin?
Digital Twin it is a technique to record and manipulate digital data obtained from physical systems, with the aim of creating a faithful digital copy of a real system. It makes it possible to study the functioning of an object, a machine, a prototype or a process. In practice, they are mathematical models of physical assets, developed from history and real-time data of the asset in question.
With a digital twin, it is possible to conduct interesting studies in digital form, without the need for such studies to be carried out in practice. This allows managers and analysts to evaluate future scenarios, simulating different possibilities in equipment, production lines and processes.
Although one of the purposes of digital twins is scenario simulation, there are some differences with respect to design simulations (theoretical simulations), such as those performed in Computer-Aided Design (CAD) software with finite element analysis, for example.
Digital Twins use real data, collected in the equipment or process being modeled, while theoretical simulations are scenario projections from generic data of materials, environments and manufacturers.
This means that design simulations are used, as its name implies, in design phases, where it is necessary to rely on nominal data to design or prototype future parts, equipment or processes. A Digital Twin has a different use, focused on creating a digital representation of a piece of equipment or process that is already in operation — and making use of data collected by sensors in real time and using it to construct a mathematical model.
Thus, a Digital Twin follows an object throughout its life cycle, not limiting itself to only one of its phases and interacting with the real world to offer always-changing and updated data. With this, it is possible to test and understand which changes will be effective, using data of the actual operation of the processes, increasing the accuracy and tracking variants over time.
This type of digital copy provides a complex and appropriate view of what is intended to replicate, without distortion. In this sense, a Digital Twin can be used in industrial production and process analysis. It be used in the simulation of future scenarios and predictive analysis, in order to adapt action planning before potential problems arise.
Overall, this technology contributes to greater transparency and visibility for companies, increasing control of managers. Thus, it becomes feasible to maintain productivity in the production environment, for example, by studying the conditions of machines, the possibilities of a product etc. This concept is, therefore, crucial to Industry 4.0.
The technologies of Digital Twins
To build a Digital Twin, you need a lot of operation data for the asset you want to model — a set that contains history data and continues to be constantly updated. The most important technology enabling this data collection process is the Internet of Things (IoT).
IoT consists of wireless sensors connected via network that send data constantly collected for monitoring. Data can be processed using edge computing technologies and stored and exhibited in cloud computing systems.
The great point of this technology is its ability to monitor assets constantly and in real time. Sensors update information at all times and can be programmed to send alerts and additional data as soon as equipment or process changes occur. Thus, operational condition analyses become richer in detail, enabling managers to identify and act on adverse conditions in a timely manner.
For Digital Twin technology, IoT is essential, as a pillar. It allows data about the replicated object to be sent to update the digital copy and allows manipulation and study of what is happening at any point in time. Therefore, by producing and sending a huge mass of data (also known as Big Data), it enables the analysis of these data with the aim of diagnosis and prediction.
Another solution that makes up the concept of Digital Twin is AI (Artificial Intelligence), which is a famous field of computer science dedicated to studying ways to generate autonomy and learning ability in computerized systems.
That means that AI allows software and hardware systems to learn and evolve like humans, making them capable of performing human tasks with greater speed. Among the AI subareas that are especially relevant to Digital Twins are Machine Learning (ML) and Deep Learning (DL).
Within the concept of Digital Twins, AI provides the cognitive ability to treat and process the data generated by Internet of Things. Based on the large amount of data captured by sensors and being sent to databases, intelligence models are able to manage this data, identify patterns and decode it mathematically to generate insights and answers.
From this information, a model can do a predictive analysis that will, for example, indicate when a problem will occur even before big signs arise. Thus, the actions of the company and management become faster and more efficient, with proactivity to reduce costs and risks.
The AI algorithms used in Digital Twins are modeled with the goal of solving complex technical challenges, such as:
- Huge data usage: AI algorithms can process volumes of data much faster than manual analytics and can reduce errors resulting from human data manipulation;
- Real-time processing speed: data is generated at high speed and in diverse schema, which requires a scalable architecture and the use of an already trained model that can process and clean the data before even performing the analysis.
Since computers can perform repetitive tasks in milliseconds, they do better at this type of task and successfully automate functions. Therefore, a Digital Twin generates data for real-time action by companies and for an even deeper study of the asset being digitalized.
AR, VR and Dashboards
IoT sensors generate the data, AI systems perform complex calculations through algorithms. For users to be able to interact with the system, a user-friendly and intuitive interface is necessary.
This interface can be simple, like a dashboard system with graphs of the main monitored parameters, their limits and their simulation controls. Process-specific layouts and real-time data visualization can also be displayed.
In addition, there are more elaborate options using VR techniques (Virtual Reality) or AR (Augmented Reality). These techniques allow users to interact with data through wearable devices and smartphones.
In Virtual Reality systems, visualization is 100% immersed in a virtual environment and there is no visualization of the physical environment. In Augmented Reality systems, on the other hand, virtual indications are positioned so that the user can see the physical world and the virtual components at the same time.
The use of these techniques can generate greater engagement in the use of the solution, as it is a technological novelty in these industrial environments. However, these techniques should be used considering suitability for operational safety — not introducing distractions in factory operations that can increase risk of accidents.
When to apply Digital Twin in industry
Due to its characteristics, a Digital Twin stands out for being high-accuracy digitization, making it an important ally for forecasts and simulations that lead to improvements in productivity, cost and risk reduction, and process automation.
However, as evidenced earlier, a Digital Twin may not always be possible or advantageous. Since it depends on a history of real, constant and up-to-date data, a Digital Twins strategy will not work if this type of data structure hasn’t been installed in the equipment or processes that one wants to model.
Sensors are essential for process automation and digital transformation of any manufacturing plant, but its adoption can take time, and require funds and time that need to be calculated and accounted for.
There are cases, too, where these types of data are simply not available. In design, creation and prototyping processes, for example, we do not have the assets for this data to be collected. In these cases, theoretical simulations continue to be used to design assets or processes being developed.
However, in propitious scenarios, a Digital Twin offers an in-depth conception of what the product is and what it can be, based on statistics. For example, in a factory scenario, the use of Digital Twins on a production line or process avoids problems of increased demand, as it is possible to simulate scenarios of higher production targets — 50 pieces per hour, for example — and to test to know if the productive capacity will be able to meet these new requirements — using real data of productivity of the equipment and process lines.
Digital Twin is a complex modeling that can capture data across all project life cycles. It allows you to create a much more faithful and updated representation of the physical asset that you want to model. This technology makes it possible to test and predict situations, to deepen knowledge and enable efficient decisions.
Venturus is a company specialized in creating advanced technological solutions for its clients. It has experts in the field of Industry 4.0 and has developed several projects focused on IoT and Artificial Intelligence.
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