Digital Twin
What is a Digital Twin?
Digital twin technology enables processes in production and industry to become more transparent, efficient and optimised. While increasing the performance of production lines, it reduces costs with applications such as predictive maintenance and process optimization. This technology is considered a critical tool that will be at the center of industrial automation and digital transformation projects in the future. Although various definitions have been made for digital twins, the basis of the definitions is the same. Some of these definitions:
“Digital Twin is the two-way integration of data between physical and virtual environments.”
“Digital Twins are virtual representations of organizing and managing resources.”
“A Digital Twin is a digital copy of assets that allows real-time two-way communication between cyber and physical domains.”
“A Digital Twin can be defined as an adaptive model of a complex physical system.” (Albayrak and Ünal, 2021).
According to the Industrial Internet Consortium (IIC), a Digital Twin is a formal digital representation of entities, processes, or systems and the appropriate behavior of that entity to communicate, store, interpret, or process within a specific context.
Types of Digital Twins
IIC identifies five different categories of digital twins based on the relationships between digital twins in systems: 1) discrete, 2) composite, 3) hierarchical, 4) relational, and 5) peer-to-peer. Abburu et al. describes three types of digital: 1) digital twins, 2) hybrid digital twins, and 3) cognitive digital twins. Every hybrid digital twin is a digital twin, and every cognitive twin is also a hybrid twin. According to another grouping, examples of types, definitions and usage areas of digital twins are listed below:
1. Component Digital Twin
Definition: It is a digital twin created for a specific component or part. It represents the smallest building block of a machine, device or system.
Area of Use: Usually used in complex parts such as a component of machines or devices, for example a motor or sensor. This digital twin monitors the component's performance and predicts maintenance and failure.
Example: A digital twin of a motor on a CNC machine constantly monitors the motor's performance and reports abnormalities.
2. Asset Digital Twin
Definition: A digital twin created for a group of components or a complete asset. This represents the overall operating status of a device or equipment.
Area of Use: Used in machines or systems containing more than one component. It monitors the interactions between components and the overall performance of the entire asset.
Example: A digital twin of a wind turbine monitors and optimizes how the turbine's blades, engine, and other components work together.
3. System Digital Twin
Definition: A digital twin that represents a complete system in which multiple entities interact. This is a broader model that covers a production line or an entire factory.
Area of Use: Used in large-scale systems, production lines, power plants or smart city systems. It monitors and optimizes the interaction of different machines, devices and processes.
Example: A digital twin of an automotive production line monitors and manages the entire production process and interaction between machines in real time.
4. Process Digital Twin
Definition: A digital twin that represents a process or workflow. It is often used to model how a particular process works and its changes over time.
Area of Use: Used in complex business processes, production operations, logistics and supply chains. It helps identify bottlenecks and areas for improvement in the process.
Example: A digital twin of a product's logistics process from factory to shipment allows monitoring performance throughout the supply chain and providing improvement recommendations.
5. Organization Digital Twin
Definition: It is a digital twin that models the entire functioning of a company or organization in a digital environment. It tracks all relationships and interactions between people, processes, assets and systems.
Area of Use: Used in large organizations or holdings. It serves the purposes of increasing efficiency, reducing costs and overall operational improvement.
Example: A digital twin that monitors and optimizes all operational processes of a factory and employee workflows.
The Relationship Between Digital Twins and Simulation
Digital twins work integrated with the physical asset and are constantly fed with data from sensors or devices. In this way, the digital twin reflects the behavior of the physical asset in real time and can be used to predict future performance. Simulators are programs that analyze how a physical system will behave under certain conditions, usually using a digital model of it. Simulators are generally independent of real-world data, allowing the model to be tested with only theoretical or assumed data. The goal is to understand and predict the behavior of the system under certain conditions.
Use of Digital Twin in Production and Industry
The use of digital twins in production and industry is becoming widespread in order to monitor and optimize production processes and increase efficiency by simulating future situations. Digital twin technology enables real-time data collection and analysis by creating digital copies of physical assets. This technology provides benefits such as increasing operational efficiency in production processes, as well as reducing costs, improving quality and optimizing maintenance processes.
Use of Digital Twin in Production and Industry
1. Production Process Optimization
Digital twins provide real-time data about machines, equipment and processes on the production line, enabling these processes to be continuously monitored and improved. All devices and machines on the production line are represented by digital twins, analyzing operational efficiency and identifying bottlenecks.
Production performance monitoring: The digital twin of each step in the production process is monitored instantly to optimize workflows.
Simulation and scenario analysis: Changes to be made in the production line are first simulated through the digital twin and the most efficient strategy is determined.
Example: On an automotive production line, the status and performance of each machine is monitored with a digital twin. In this way, a faulty or slow-running machine can be quickly detected and intervened in.
2. Predictive Maintenance
Digital twins allow continuous monitoring of machines and equipment used on the production line. The performance of the machines is monitored with the data coming from the sensors and possible malfunctions are predicted before the problem occurs. This goes beyond planned maintenance, minimizing the risk of breakdowns and preventing production interruptions.
Failure prediction: The digital twin monitors the condition of the equipment and detects performance degradation and predicts the possible failure date.
Cost optimization: Predicting malfunctions reduces unplanned downtime and maintenance costs.
Example: Digital twins of CNC machines used in a production line are constantly monitored with data from sensors, and situations such as decrease in engine performance and overheating are instantly detected and the machine maintenance team is warned.
3. Ürün Tasarımı ve Geliştirme
Dijital ikiz teknolojisi, ürün geliştirme süreçlerinde tasarımların sanal ortamda test edilmesini sağlar. Bir ürünün dijital ikizi oluşturularak, fiziksel prototipler üretmeden önce performans ve dayanıklılık analizleri yapılabilir. Bu, tasarım süreçlerini hızlandırır ve maliyetleri önemli ölçüde düşürür.
Simülasyon ve test: Ürün geliştirme sürecinde, dijital ikiz ile ürün performansı farklı koşullar altında simüle edilerek test edilebilir.
İyileştirilmiş tasarım döngüsü: Tasarım hataları erken aşamada tespit edilerek hızlı bir şekilde düzeltilebilir ve ürünün pazara çıkış süresi kısaltılabilir.
Örnek: Bir uçak motorunun dijital ikizi, farklı uçuş koşullarında nasıl çalışacağını simüle ederek tasarım aşamasında sorunları ortaya çıkarır. Bu sayede motorun fiziksel prototipi üretilmeden performans testleri yapılabilir.
4. Quality Control and Traceability
Digital twins improve quality control stages in production processes. A digital twin of each product produced on the production line can be created, and in this way, the stages that each product went through and under what conditions it was produced can be recorded. This helps quickly detect production errors and provide traceability.
Real-time quality monitoring: Instant quality control can be performed during production processes and faulty products can be quickly detected.
Backtracking and analysis: Errors and problems that occur during the production process are analyzed and corrected retrospectively.
Example: In a factory producing electronic components, a digital twin of each product is created and the product's production conditions, materials used and test results are recorded. If a component turns out to be faulty, the source of the error can be determined based on this information.
5. Supply Chain and Logistics Management
Digital twins can be used to optimize material flow and logistics processes in the supply chain. All stages of products from production to reaching the customer can be monitored through digital twins. This helps identify delays and bottlenecks in the supply chain.
Logistics and material flow monitoring: It can be checked whether the materials required for production are available at the right time and place.
Warehouse and stock management: Stock optimization can be done by monitoring the inventory level in storage areas with digital twins.
Example: In an automotive factory, shipments of parts in the supply chain are tracked with digital twins. If parts do not reach the factory on time, production stoppages can be detected in advance and intervened.
Benefits of Using a Digital Twin
Efficiency Increase: By monitoring and optimizing production processes instantly, efficiency is increased and resource waste is reduced.
Faster Decision Making: Fast and accurate decisions can be made based on real-time data.
Cost Reduction: Costs are reduced thanks to failure predictions and optimization of maintenance processes.
Increased Product Quality: Performing quality control processes through digital twins minimizes the number of defective products.
Flexibility: Changes to be made in production lines can be planned without damaging production by testing on the digital twin.
Important Technologies Used in Digital Twins
The successful creation and operation of digital twins relies on the integration of various advanced technologies. In order for digital twins to work effectively, many technologies such as IoT, big data, artificial intelligence, cloud computing, simulation and advanced sensors are used together. Integration of these technologies enables digital twins to remain constantly connected to real-world assets and processes and contribute in key areas such as performance, efficiency, maintenance and optimization. Relevant technologies, their roles in a digital twin, and examples are listed in the subsection
1. Internet of Things (IoT)
Description: The Internet of Things (IoT) enables physical devices to collect data by connecting to the internet through sensors and other data collection tools.
Role in Digital Twin: In order for digital twins to work, real-time data from physical assets is needed. This data is collected by IoT devices and transferred to the digital twin. IoT enables digital twins of machines, equipment and processes to be monitored and fed with data in real time.
Example: In a factory environment, IoT sensors collect data such as temperature, pressure, vibration from machines on the production line and send it to the digital twin. The digital twin analyzes machine performance using this data.
2. Big Data and Analytics
Description: Big data technology enables the collection, storage and analysis of large and diverse data sets. Analytical tools are used to derive meaningful insights from this data.
Role in Digital Twin: Digital twins continuously collect and process large amounts of data. Big data technologies enable this data to be stored and analyzed effectively. Digital twins rely on big data technologies for real-time analysis, performance predictions and optimization scenarios.
Example: In a power plant, large amounts of data collected from different sensors are analyzed in the digital twin to optimize energy production efficiency.
3. Artificial Intelligence and Machine Learning (AI/ML)
Definition: Artificial intelligence (AI) and machine learning (ML) use algorithms that can make predictions by analyzing large data sets and improve themselves over time.
Role in Digital Twin: Artificial intelligence and machine learning enable digital twins to learn from data and perform predictive analysis. These technologies help digital twins perform tasks such as failure prediction, process optimization, and behavior modeling.
Example: A machine's digital twin uses machine learning algorithms that analyze machine performance data to predict when it might fail and provide proactive maintenance recommendations.
4. Simulation and Modeling Technologies
Description: Simulation technologies enable physical processes, devices or systems to be modeled in a digital environment. Simulation is used to predict how systems will operate and to test different conditions.
Role in Digital Twin: Digital twins rely on modeling technologies to create and simulate digital models of physical entities. In this way, the digital twin simulates situations that may be encountered in the real world and can be used in decision-making processes.
Example: A digital twin of a production line can determine the most efficient production strategies by simulating the impact of changes in the production process on productivity.
5. Cloud Computing
Definition: Cloud computing is the provision of applications that require large data storage and processing power via server infrastructures over the internet.
Role in Digital Twin: Digital twins collect and analyze large amounts of data. This data is stored and processed on cloud platforms. Cloud computing is used to meet the high processing power requirements of digital twins, storing large data sets and making them accessible from different locations.
Example: In a smart city project, the city's digital twin runs on the cloud, enabling optimization of processes such as traffic, energy consumption and infrastructure management.
6. Cyber Physical Systems (CPS)
Description: Cyber physical systems (CPS) combine physical assets with the digital world, enabling real-time data flow and interaction between the two worlds.
Role in Digital Twin: Digital twins are in constant communication with physical systems through CPS technologies. CPS enables digital and physical systems to influence each other in real time, so the digital twin can instantly react to real-world changes.
Example: A digital twin of an autonomous vehicle can adapt the vehicle to road conditions and traffic conditions in real time.
7. Advanced Sensors and Actuators
Description: Sensors collect data from the physical world, while actuators act on physical systems based on this data.
Role in Digital Twin: Digital twins monitor the condition of the physical asset using data from sensors. Actuators, on the other hand, can change or control the physical entity based on analysis made through the digital twin.
Example: A digital twin of a robot arm monitors and optimizes the arm's movements based on data from sensors. Actuators control the movements of the arm.
8. Augmented Reality (AR) and Virtual Reality (VR)
Description: AR and VR technologies provide users with more realistic and interactive experiences by enabling interaction between digital and physical worlds.
Role in Digital Twin: When used with AR and VR technologies, digital twins visualize the states of physical systems and enable users to interact in a virtual environment. This is especially beneficial in training and maintenance processes.
Example: In a factory, maintenance technicians can view a digital twin of a machine with AR glasses and follow maintenance operations step by step.
9. Blockchain and Distributed Ledger Technologies (DLT)
Definition: Blockchain is a technology that provides decentralized, secure and immutable data records.
Role in Digital Twin: In digital twins, blockchain technology enables secure tracking and recording of digital assets and processes. It enables digital twins to exchange secure data, especially in areas such as supply chain management.
Example: A product's digital twin provides transparency and traceability by recording the product's history on the blockchain throughout the supply chain.
10. 5G and Advanced Communication Technologies
Description: 5G is a new generation network technology that provides high-speed and low-latency wireless communication.
Role in Digital Twin: Advanced communication technologies such as 5G, which offer high speeds and low latencies, are needed to meet the real-time data flow and control requirements of digital twins. In this way, digital twins can monitor and control much larger and more complex systems simultaneously.
Example: Thanks to 5G, digital twins of autonomous vehicles monitor traffic and road conditions in real time and can make quick decisions.
References
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