Demystifying Digital Twins in Global Electronics

Digital Twin technology offers incredible capabilities during a product’s design and engineering phase. Whether it’s consumer electronics, aerospace, defense, Medtech, or other industries, digital twins lead to better products. 

As part of the Siemens Xcelerator open digital business platform, Supplyframe’s solutions provide critical intelligence and insights that can be utilized alongside digital twins in a broader connected digital thread. Join us as we discuss this exciting technology and its future. 

What are Digital Twins?

Digital twins are virtual representations, or digital counterparts, of physical objects, systems, or processes. They are created using real-time data to simulate, predict, optimize performance, monitor operations, and facilitate decision-making. 

Digital twins mirror their physical counterparts’ behavior, characteristics, and performance. Companies across various industries utilize digital twins, including manufacturing, healthcare, transportation, and energy. 

Digital twins remove the need for physical prototypes, which can reduce development time and cost. They also improve a product’s quality by combining simulation, data analytics, and machine learning, which allows designers and engineers to measure performance across many use cases and environmental conditions. 

The use of digital twins dates back to the 1960s when NASA pioneered digital twin technology in space exploration missions. Each spacecraft mission was replicated in an earthbound version used by NASA personnel serving on flight crews for study and simulation purposes.

There are three major types of digital twins:

• Product digital twins replicate physical products in digital form. They are applied in product design, testing, and simulation. The three digital twins and their inherent processes are known as a digital thread, which weaves through all stages of the product and production lifecycles.
• Process digital twins simulate and analyze the behavior of physical processes or systems. They can monitor, control, and optimize the operations of complex systems in manufacturing plants, supply chains, and energy grids.
• System digital twins replicate entire systems or ecosystems in a digital environment. They integrate multiple digital twins of products, processes, and other components to simulate complex systems’ behavior comprehensively.

Product digital twins of manufacturing equipment and products are designed to optimize production schedules, predict equipment failures, and improve overall efficiency. 

Digital Twins Across Industries 

Digital twins are designed to optimize traffic flow and energy consumption and enhance public services in urban infrastructure, transportation networks, utilities, and healthcare. For example, in the healthcare sector, digital twins of a patient’s physiology are used to create a personalized treatment plan that remotely monitors the patient’s health metrics and simulates surgical procedures.

In the energy sector, digital twins are designed to optimize energy production, predict equipment failures, and manage grid stability of power plants and renewable energy systems. Other digital twins in the energy sector include simulations or digital models.

How are Digital Twins Used in the Real World? 

Simulation software replicates the behavior and performance of physical systems in various conditions. It incorporates digital twin modeling by integrating real-time data, physics-based models, and simulation techniques to represent the physical asset visually. 

This includes simulating the physical system’s dynamic behavior, interactions, and performance characteristics. Based on underlying physics principles, simulation software analyzes and predicts the system’s behavior.

In practice, digital-twin modeling often involves a combination of CAD and simulation software. CAD software creates a virtual representation of the physical asset, while simulation software simulates the behavior and performance of the digital twin. 

Integrating CAD and simulation software allows engineers to create comprehensive digital twins that accurately represent the physical system and its dynamic behavior. 

Furthermore, some software platforms offer integrated solutions that combine CAD and simulation capabilities into a single platform, allowing users to transition from design to analysis within the same environment seamlessly. These integrated solutions enable engineers to create, simulate, and optimize digital twins more efficiently and effectively. 

5 Use Cases for Digital Twin Technology 

1) Physics-based models

A physics-based executable digital twin relies on mathematical models that describe the physical behavior of the system being replicated. These models are typically based on fundamental physics principles, such as mechanics, thermodynamics, fluid dynamics, electromagnetics, etc. By solving the equations that govern these physical phenomena, the digital twin can simulate the behavior of real-world systems in a virtual environment.

2) Simulation of physical processes

The digital twin simulates the physical processes and interactions within the system using physics-based models. This allows it to predict the system’s behavior under different operating conditions, inputs, and scenarios.

3) Real-time simulation

An executable digital twin based on physics models can simulate the behavior of the physical system in real-time or near-real-time. This enables dynamic interaction and decision-making based on the current state of the system and its environment.

4) Closed-loop control

Physics-based executable digital twins often operate in a closed-loop control system, where real-time data from sensors and actuators are used to adjust the simulation parameters and control the behavior of the virtual model. This allows the digital twin to maintain desired operating conditions and optimize performance.

5) Validation and verification

Physics-based models used in executable digital twins must be verified to ensure their accuracy and reliability. This involves comparing simulation results with real-world measurements and experimental data to confirm that the digital twin accurately represents the physical system.

While physics-based modeling is commonly used in executable digital twins, other modeling approaches, such as data-driven modeling, empirical models, or hybrid models combining physics and data-driven techniques, may also be employed depending on the application’s specific requirements and constraints.

What’s Next for Digital Twin Technology? 

Executable digital twins (xDTs) represent the next evolution in digital twin technology, offering enhanced capabilities for real-time simulation, decision-making, and optimization of physical assets and systems. 

These digital twins are dynamic models that respond to inputs, simulate scenarios, and make decisions autonomously or with human intervention. Simply put, xDTs are digital twins on a chip. 

They use data from a relatively small number of sensors embedded in the physical product to perform real-time simulations using reduced-order models. From those small numbers of sensors, xDTs can predict the physical state at any point on the object, even where sensors would be impossible to place.

Real-time simulation and interaction can replicate the behavior and performance of a physical asset or system in real-time. They can respond to inputs, simulate operating conditions, and dynamically interact with external systems or users.

xDTs can make decisions autonomously based on predefined rules, algorithms, and machine learning models. They can analyze data, predict outcomes, and take actions to optimize performance or respond to changing conditions. 

Breaking Down The Types of Executable Digital Twins (xDTs) 

1) Closed-loop control xDTs often operate in a closed-loop control system, where real-time data from sensors and actuators are fed back into the virtual model to adjust parameters, optimize performance, and maintain desired operating conditions.

2) Predictive analysis and optimization. xDTs use predictive analytics and optimization techniques to forecast future behavior, identify potential issues or opportunities, and recommend actions to improve performance or mitigate risks.

3) Integration with IoT and AI technologies. xDTs leverage the Internet of Things (IoT) sensors, connectivity, and artificial intelligence (AI) algorithms to collect real-time data, analyze complex patterns and make informed decisions. They may also incorporate machine learning models for adaptive behavior and continuous improvement.

4) Dynamic adaptation and learning. xDTs can learn from experience and adapt to changes in the environment or operating conditions over time. Based on new data and feedback, they can continuously update their models, parameters, and strategies.

5) Executable digital twins find applications across various industries, including manufacturing, energy, transportation, healthcare, and smart cities. They enable predictive maintenance, autonomous operation, process optimization, and decision support in complex systems where real-time monitoring and control are critical. 

An executable digital twin is an advanced digital twin that represents a virtual replica of a physical asset or system and can execute, simulate, and interact with the virtual model in real-time.

Intelligence for What’s Next 

As part of the Siemens Xcelerator open digital business platform, Supplyframe’s industry-leading Design-to-Source Intelligence solutions seamlessly integrate into other Siemens software solutions to form cohesive digital threads. 

Bridge the gaps between design, sourcing, procurement, and production. Visit Supplyframe.com today to learn more!