Digital Twin in the Context of Industry 4.0 - Visartech Blog

In the wake of Industry 4.0, businesses are undergoing significant tech-driven changes. From corporate giants to small companies, smart technologies are being integrated into business workflows on a massive scale. One of the many new business solutions that have emerged from Industry 4.0 is the virtual twin. This technology is now leading the digital makeover of the global industry.

With a proven track record of developing high-quality system twins, Visartech has established its expertise in the field. One of its standout digital twin business cases is the golf simulation platform. By creating a virtual twin of the golf game, we helped a client create a single sports simulation solution. This platform garners recognition every year and attracts an expanded user base.

Through this article, we will explore the various aspects of digital twin solutions. Namely, their benefits and costs, and the components that go into creating them. By sharing our knowledge, we hope to help you harness the power of this technology and stay ahead of the competition.

To understand the importance of virtual twin technology, we must first understand the key technologies powering Industry 4.0. These include:

• Augmented Reality (AR)

AR technology uses digitally created sensory information to enhance the physical world. In Industry 4.0, AR is being used to improve worker safety, increase efficiency, and enhance collaboration between humans and machines.

• Big Data Analytics

The process of analyzing and uncovering patterns, trends, and insights from large and complex data sets. This data helps to optimize production processes, improve product quality, and reduce downtime.

• IoT (the Internet of Things)

The Internet of Things refers to the network of physical devices embedded with electronics, software, and sensors. This allows for collecting and sharing real-time data, monitoring equipment and processes, and optimizing production.

• Cybersecurity

With the increasing use of digital technologies in Industry 4.0, information security has become more critical than ever before. Information security technologies protect manufacturing plants from cyber threats and ensure the safety and security of sensitive data.

• AI & Machine Learning

These technologies are the current trend of automation and data exchange in manufacturing and other industries. With the ability to analyze vast amounts of data and learn from it, AI & ML improve efficiency in production processes. From predictive maintenance to quality control.

• Digital twins

Virtual copies of processes, production lines, plants, and supply networks. They are generated with the help of data gathered from IoT devices.

Let’s explore the concept and processes behind virtual twin solutions and gain a fresh perspective on the industry.

A digital twin often referred to as a virtual twin, is considered the most recent concept among such tech-driven innovations. Although it was first used about two decades ago, it has only lately gained popular acceptance.

According to 87% of CEOs, digital twins are increasingly crucial to their company’s capacity to cooperate in strategic ecosystem alliances. Furthermore, 65% of executives expect their company to increase its investment in virtual twins over the next three years.

This digital solution helps manufacturers create new products, optimize processes, and boost production. Moreover, digital replicas allow them to better understand the products, including their current and future market performance.

Companies may also eliminate existing barriers with the following features of digital twins:

• Digital model;
• 3D representation;
• Digital twin simulation;
• Document management system;
• Visual representation;
• Model synchronization;
• Connected analytics.

The main idea behind this virtual twin solution is predicting and fine-tuning performance using simulation- or data-based methods.

… a tool that allows for testing different what-if scenarios before bringing any changes to the current state of the company’s affairs.

The digital twin solution is a virtual space, wherein you can test your ideas. However, a 3D digital twin can only be a major asset if it can make precise predictions. This means that a virtual twin is necessary for

• Understanding and modeling the performance of an asset;
• Forecasting its behavior;
• Optimizing its use and maintenance practices.

Therefore, this smart business solution serves as a link between the real and virtual worlds.

Digital twins are becoming more common in various industries, and understanding their components is crucial for their successful implementation. Let’s dive into the different components of a digital twin and how they function together.

But first, let’s look at the digital twin components to understand how it functions:

• AI

The use of AI is essential to create digital twins that can act efficiently on behalf of their real counterparts. The controller of the digital twin is integrated with ontologies, machine learning, and deep learning methods.

• Unique identifiers

Each digital twin needs a unique identifier to connect with its physical twin. This identifier enables the twin to receive data from sensors, which it can then analyze and use to make decisions.

• Sensors & actuators

To mimic the senses of sight, hearing, taste, smell, and touch, digital twins need to be fitted with sensors. Actuators help them to respond to the data collected by the sensors effectively.

• Communication

Digital twins need to be able to communicate with their environment. They achieve this by following the Tactile Internet and 5G standards, which include the ability to sense touch.

• Digital platform

The integrated service platform combines information about the system, software tools, and models. It unifies services for the physical and virtual shop floor.

• Representation

Based on various digital twin applications, they can be software components without a physical representation. Also, they could have a virtual representation as a 3D avatar, hologram, or even a humanoid social robot.

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• Privacy & security

To ensure the privacy and security of the physical twins, advanced biometrics and encryption methods are utilized. This helps to mitigate legal and political difficulties.

More than two-thirds of firms have implemented IoT. By 2022, they should have at least one digital twin deployed in production, predicts Gartner. Experts predict that the market for using twins in manufacturing will reach over $6 billion by 2025.

Virtual twins can take many forms, but they are most valuable when used as digital twins of an organization. A complex digital twin model is capable of combining process and contextual data. It can provide insights into how your organization utilizes its business model, adapts to changes, and delivers value to customers.

With a true enterprise-grade digital twin solution, you can simulate the dynamic operation of your company’s whole system.

Digital twins have become the go-to tool for businesses looking to detect issues timely and boost productivity. Some of the largest companies in the world are already reaping the benefits of using virtual copies in various ways.

Siemens, a tech giant, integrated a 3D digital twin solution into its three main product lifecycle phases: manufacturing, performance, and product. These based simulations help optimize production and identify and prevent potential mistakes or failures before real production begins.

What’s more? Siemens teamed up with vaccine producers. They scaled up vaccine manufacturing using digital twin technologies.

Every self-driving vehicle constructed by Tesla is expected to have a digital twin. It allows synchronized data to transfer between the factory and the vehicle. Moreover, by choosing virtual twins, Tesla concentrated on enhancing manufacturing services, prognostics, and health management in real time.

Other companies, like General Electric (GE), use the digital twin to generate predictions of the health and performance of their products. Remarkably, GE created the virtual twin of a wind farm to improve maintenance and operation processes. This digital business solution also helped the company boost energy production.

Besides, GE used virtual twins to track the lifetime of locomotives. It also created the industrial digital twin for hospital bed planning and job allocation optimization. The PREDIX Digital Twin platform was also released by GE to anticipate the performance of industrial assets.

In the aviation sector, Airbus is a case in point. The company used digital twins to simulate real-world circumstances, discover flaws, and foresee and address probable issues of aircraft maintenance.

The Iron Bird is a giant ground-based test rig created by Airbus Industries in 2015. The goal was to integrate, optimize, and validate critical aircraft systems. Digital twin allows for the testing of components before the physical ones are available, even before the maiden flight.

Digital solutions are changing the way businesses operate. They provide a comprehensive solution that can significantly improve R&D, provide accurate insights, facilitate planned maintenance, and optimize workflows.

Let’s explore the incredible advantages that digital twins offer and how they can benefit businesses of all sizes.

Digital twins produce a wealth of data that can help businesses make necessary product advancements before production. By employing digital twins in product development, developers can foresee how a development cycle will turn out and monitor production systems. Digital twins can even come up with improvements for a product that is nearing the end of its existence.

The creation of a digital twin system offers more thorough support for making decisions. You can assess current conditions, diagnose past issues, and forecast future trends. Moreover, digital twins provide situation awareness and real-time insights synced with physical entities. That helps make decisions based on the true data.

Digital twins can help with planned maintenance. Having a digital copy of a physical asset or system, maintenance teams will be able to analyze data. Thus identifying any possible issues before they become a problem.

This proactive approach to maintenance can reduce downtime, increase efficiency, and ultimately save businesses significant amounts of time and money. With digital twins, maintenance teams can also simulate different scenarios to test potential solutions. Thereby avoiding the risk of damaging the physical asset.

Digital twins can efficiently optimize a physical asset along with collecting sensory data, AI, and big data analytics. By introducing new business strategies or enhancing workflow through digitization, virtual twins can impact digital transformation for companies.

On the other hand, digital twins can monetize and apply real-time information to alter the business model of an organization. This is why companies start exploring methods to commercialize and optimize these new digital assets.

Digital twin solutions offer a vast array of benefits to businesses across various industries. However, it’s essential to keep in mind that not everything needs to be digitally twinned, nor does every business require an enterprise digital twin.

Before embarking on digital twin development, companies must carefully analyze which digital twin type will provide the best benefit for their operations.

Component Twin & Part Twin

A single system component can be virtually represented by component twins. This directly affects the functionality and performance of the system. Although they refer to less important parts of a system or product, part twins follow the same principle.

These types are popular among component manufacturers, as they facilitate engineers’ understanding of the product’s main features. An excellent example of a component digital twin is the project Visartech worked on. The team helped the client create a 3D virtual representation to investigate a 3-axis scanner designed for weld inspection or line scanning.

Asset Twin

Also known as product twins, asset twins demonstrate how some product components interact. They produce a plethora of performance data. It can be analyzed to provide insightful knowledge. Digital twinning of certain parts of the system enables its optimization.

A case in point is an asset Twin of the wind turbine. It will help to monitor how it performs and spot potential component breakdowns.

Process Twin

The term “process twin” stands for a digital representation of networked systems. This particular type of virtual twin tells if all these systems are synced to function as effectively as possible. It can also indicate if the system is delayed and how it may impact others.

For instance, a VR-based business process solution our development team created for an automotive company. This twin is a digital representation of the company’s processes and how they interact in a networked system. Vizualized resource intensity of each process in real-time allows for data-driven decisions and optimization of the company’s overall operations.

System Twin

These twins examine the correlation between several assets that create a complete, functional system. They explain how assets interact and may offer performance improvements.

Consider auto manufacturing as an illustration. Here, a system twin combines all the units required for producing a certain final automotive component.

As mentioned above, a digital twin is a virtual counterpart of a physical system or thing. While a digital thread is the digital record of an end-to-end product’s lifecycle.

To figure out the main difference between digital twins and digital threads, let’s look at some examples.

A case in point is a digital twin of a wind turbine. Having such a digital twin, engineers predict how it will behave in different weather conditions, and identify any potential issues before they occur. Thus optimizing the performance of the turbine and reducing maintenance costs.

Going back to a digital thread. For example, a car manufacturer uses a digital thread to track the development of a new vehicle, from the initial concept to the final product. This allows them to identify any potential issues early on, and make changes to the design before it goes into production. It also enables them to ensure that their product meets all regulatory requirements.

So the key differences between the digital twin and digital thread include the following.

Digital Twin	Digital Thread
Virtual replica of a physical object	Digital representation of the full product lifecycle
Simulates the performance of a physical object	Tracks the entire product lifecycle
A single snapshot of a system	A continuous stream of data tracking the product lifecycle
Used for design optimization and predictive maintenance	Used for quality control and regulatory compliance

The concept of the digital twin stimulated the development of many digital transformation solutions. They now underpin most business models and the growth of modern industries. However, the question is how to create a digital twin model, and what is the cost of this process?

First and foremost, any digital twin requires the storage of vast amounts of data. The final cost of creating a digital twin solution depends heavily on 2 aspects. The type of IT infrastructure and the volume of data.

Here are the main aspects when considering digital twin creation.

One core feature of digital twin engineering is the ability to produce a 3D model of shop-floor resources. This virtual representation can come in various types, such as geometric models, physical models, and system behavior models. Depending on the application’s requirements, a single digital twin system can support multiple types of virtual models.

Digital twin architectures use data analytics techniques. They analyze data acquired from physical systems and virtual data offered by the twin model. This requires thorough analytics support covering various techniques such as prescriptive analytics, optimization analytics, and predictive analytics. The value of robust analytics support lies in enabling businesses to adapt to change faster, providing them with a competitive edge.

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They are also crucial in digital twin solutions. For an accurate digital twin, the shop floor’s current status must match the modeled pieces. Additionally, frequent data transfers from sensors to the digital twin’s data storage layer are necessary for near-real-time updates.

A digital twin requires data to recognize, react to, and adapt to the surroundings and operating circumstances. Tools for managing this data can be divided into five categories. They include data collecting, data transmission, data storage, data processing, data fusion, and data display. To accumulate data from different sources, both digital and physical, creating a centralized repository is essential.

By considering these key features, businesses can get a full picture of the financial matter and make informed decisions.

In conclusion, digital twins have emerged as a powerful technology that can help businesses drive digital transformation in Industry 4.0. Creating a digital twin model may cost a lot. Yet, by partnering with experienced professionals, businesses can create a digital twin that is cost-effective and efficient.

Digital twins offer a range of benefits, including optimized production processes and product design. They can help businesses become more agile and intelligent, driving innovation and growth in modern industries. By beginning to create digital twins today, businesses can position themselves as leaders and ensure they stay ahead of the competition.

Wanna advance your company’s processes by using digital twins? Connect with our experts to discuss the details!