Digital twin and AI - key concept for Industry 4.0

The rapidly expanding market for digital twins indicates that they are already widely used in various industries - with demand forecast to remain high in the future. In 2020, the market was valued at USD 3.1 billion and could grow to an estimated USD 48.2 billion by at least 2026, according to industry experts. The comprehensive use of digital twins makes it possible to minimize operational downtime and optimize production at the same time.

Market analysis

The rapidly growing market for digital twins is proof that they are already being used in many industries, but that demand will continue to grow strongly over a longer period of time. A market size and market share analysis for digital twins by Mordor Intelligence shows that the market for digital twins was already valued at USD 3.1 billion in 2020. Some industry analysts speculate that it could continue to grow to an estimated value of USD 48.2 billion by at least 2026. The End-to-end application of digital twins enables companies to reduce system downtimes and increase production at the same time.

What is a digital twin?

A digital twin, often also referred to as a "digital twin", is a computer-aided model of a physical or non-physical object that is used in a variety of ways. Sometimes the term "digital avatar" is also used.

Explanation of terms

The digital twin is a virtual model, for example of a

• Process
• Product
• a service

and links the real world with the digital world. Digital twins use real data that comes from installed sensors and records, for example, operating conditions or the position of machines. This combination of real and virtual worlds enables the Data analysis and monitoring systems to detect and deal with problems at an early stage, avoid downtime, create new opportunities and plan future scenarios using computer simulations.

Nowadays, digital twins that cover the entire life cycle of a product, process or business model and thus form the basis for networked products and services are a business necessity.

Function

Three components are required for a digital twin:

1. The real object.
2. The digital twin in virtual space.
3. The information that connects the two.

The object is equipped with sensors that collect data on its status or position, which is then received, processed and analyzed by a system. Digital twins therefore improve the operational and financial performance of an object such as a plant or service. Technical experts usually create these and also use the real-time data of the objects.

Benefit

A digital twin can be useful throughout the entire life cycle of an object. In the first phase "Design", it supports the handling of complex product requirements, fast development cycles and strict regulatory requirements. The digital twin helps to examine different design alternatives and carry out simulations and tests to ensure that requirements are met. In the "production" phase, the twin can increase efficiency, quality and yield. During the "operation phase", a digital twin improves the availability of objects such as machines. In the "recycling phase", the digital twin can be used to plan spare parts or identify upcycling potential.

Benefits of the digital twin in the manufacturing industry

From control improvement and product optimization to process monitoring: Companies use product-related data and predictive simulations to increase production efficiency and product quality while reducing manufacturing and service costs. This creates capacity to meet future challenges competitively:

• Product development - A digital twin of a product is used for virtual tests and simulations to improve the design, predict performance and identify product defects at an early stage. This leads to lower development costs and a faster time to market.
• Maintenance and servicing - The digital twin provides real-time information about the condition and performance of a product. Data analyses can be used to detect anomalies or wear and tear and initiate maintenance work. This reduces downtime and extends the service life of products.
• Process optimization - Digital twins are used to optimize manufacturing and business processes. The analysis of production data enables bottlenecks to be identified and processes to be improved, leading to cost reductions and increased productivity.
• Virtual commissioning - With the help of a digital twin, it is possible to test systems and production lines virtually before they are actually built. This enables weak points to be identified and rectified at an early stage, saving time and money.
• Education and training - Digital twins are also used to train and educate employees. Complex tasks can be learned and specific scenarios practiced in a virtual environment. This reduces training costs and increases the effectiveness of training.

Submodels in industrial production and engineering

• Model-based self-descriptions - These aim to enable auto-identification and auto-configuration so that, for example, machines and their components can register independently in the MES system or IoT system with their capabilities and services using the driver information provided.
• Description of capabilities (skills) of production systems - These include specific manufacturing processes such as turning or MAG welding as well as material flow functions such as lifting or continuous conveying. Attributes and their permissible value ranges are also included, as well as the logic in some cases. This information can be used to efficiently assemble, configure and commission production equipment to create new systems for specific manufacturing tasks.
• Data-based models of normal behavior - These models are based on the runtime data of a machine, a line or an entire production process, which is recorded from real operations via an MES, for example, and analyzed using machine learning.
• Offline and online simulations - This includes specialized simulators, such as those for finite elements, virtual commissioning or the simulation of physical processes. Ideally, these simulation models interact with each other. Although the term digital twin is often equated with simulation, this definition is too narrow, according to Fraunhofer IOSB.
• The digital factory - This comprises a comprehensive network of digital models, methods and tools that are integrated through end-to-end data management, for example for production and material flow systems as well as buildings and technical building equipment (in accordance with VDI 4499, Sheet 1).

A comprehensive digital twin also includes aspects such as IT security, access rights, certificate handling, version management and compatibility tests of different versions of digital twins.

Connection between the digital twin and Industry 4.0

The digital twin is used in production by combining product data with models as well as operating and process data to create a comprehensive and dynamic virtual image of a real product. This combination forms the basis for retrospective analyses and future-oriented predictions regarding the product.

The continuous enrichment of the digital twin with current measurement data creates a detailed digital image that enables reliable statements and forecasts. This supports the development of services such as tracking products, carrying out usage analyses or creating predictive maintenance strategies in order to plan maintenance efficiently and in good time.

Digital twins therefore play a decisive role in Industry 4.0 and the digitalization of production.

The "digital twin concept"

At the beginning of the 21st century, M. Grieves and J. Vickers further developed the basic concept of the digital twin as it is used today (see Function of a digital twin). The concept consists of three main elements: The physical products in real space, the virtual products in digital space and the connecting data and information flows.

This extended concept enables communication between the real and the virtual object, whereby the data from the real to the virtual object is referred to as a "digital shadow" and the return information as a "digital pulse".

This interaction allows the adaptation of real objects based on the virtual models, which has a positive effect on more efficient process control and preventive measures through real-time data. The definition of the digital twin therefore also includes various archetypes, ranging from a simple digital representation to autonomously controlled, interactive systems. Digital twin archetypes:

• Basic Digital Twin - Simple digital representation with internal data processing.
• Enriched Digital Twin - Inclusion of neighboring data streams.
• Autonomous Control Twin - Autonomous system with human-machine interface.
• Enhanced Autonomous Control Twin - Advanced autonomous control with data downstream.
• Exhaustive Twin - Comprehensively integrated digital twin that incorporates all relevant data and processes.

Types of digital twins

Depending on the level of detail, there are various types of digital twins, which differ mainly in their area of application. It is common for several types of digital twins to be used simultaneously within a system or process. The various types of digital twins and their specific differences and areas of application are described in detail below:

Component twins/partial twins
These form the basic unit of a digital twin and represent the smallest example of a functional component. Partial twins are designed in a similar way, but relate to less critical components.

Asset twins
When two or more components interact, a so-called asset is created. Asset twins allow these components to interact by analyzing and generating a large amount of performance data that can be further processed and transformed into useful insights.

System or unit twins
These represent an extended level and make it possible to track how different assets form a functioning overall system. System twins offer a clear view of the interactions between assets and can provide recommendations for improving performance.

Process twins
At the macro level, process twins reveal how different systems work together to operate a complete production plant. They check whether these systems are synchronized and operating at maximum efficiency, or whether delays within one system have an impact on other systems. Process twins are helpful in developing precise schedules that impact overall manufacturing effectiveness.

Market and sectors for the digital twin

Digital twins are valued for their performance, but their use is not appropriate for every manufacturer or every product. Some objects are not complex enough to justify the intensive and continuous flow of sensor data required for digital twins. In addition, creating a digital twin can be challenging from a financial perspective, as accurately replicating a physical object is potentially costly.

Nevertheless, certain types of projects benefit considerably from the use of digital models:

• Large-scale projects with physical dimensions - For example, buildings, bridges and other complex structures that are subject to strict engineering standards.
• Mechanically complex projects - These include jet turbines, cars and airplanes, where digital twins can help to increase machine efficiency.
• Power supply systems - These include both power generation and power transmission mechanisms.
• Manufacturing projects - Digital twins help to optimize the efficiency of processes, especially in industrial environments with cooperating machine systems.

Industries that have achieved significant success with digital twins include:

• Engineering
• Automotive engineering
• Aircraft construction
• Rail vehicle construction
• Building industry
• Manufacturing
• Energy supply company

These sectors benefit from digital simulation and improved operation through the use of digital twins.

Read more information on industry-related applications in the section: Areas of application for digital twin systems

Advantages of digital twins

Improvement in research and development (R&D)
The use of digital twins increases the effectiveness of product research and development. They provide extensive data on the expected performance of products, enabling companies to gain important insights. This information helps companies to implement necessary product improvements before the start of production.

Increasing production efficiency
After products have been transferred to production, digital twins enable production systems to be mirrored and monitored. This helps to achieve and maintain the highest possible efficiency throughout the manufacturing process.

Support throughout the product life cycle
Digital twins also support companies in making decisions regarding the further processing of products at the end of their life cycle, be it through recycling or other measures. They make it possible to determine which materials can be reused and thus contribute to the efficient use of resources.  

Application areas of digital twin systems

Areas of application for digital twin systems and how Konfuzio provides efficient support for their implementation:

Industrial manufacturing and production control

As mentioned in the sections above, digital twins play a central role in industrial manufacturing by optimizing design and production processes through simulation and analysis. They support companies throughout the entire life cycle of products, systems and services, improve value creation and enable the manufacture of networked products. They are increasingly being used in production and order management to optimize processes and integrate them with control and regulation technologies.

Support from Konfuzio
Konfuzio offers advanced AI-based solutions that analyze and process data in real time to increase production efficiency and optimize the entire production process. This includes the Automation and precise control of production processes.

Transportation and logistics

In the transportation industry, large logistics companies use digital twins for functions such as track and trace and the intelligent control of warehouses or port facilities. These technologies are also being integrated into ERP systemsin order to Digital supply chain and optimize supply chain management.

Support from Konfuzio
With its AI-based solutions, Konfuzio enables more efficient logistics and improves the tracking and management of goods through precise data analysis and the resulting forecasts.

Medicine and healthcare services

Digital twins are also used in medicine by creating virtual images of patients to simulate medical procedures and plan treatments. They also support individualized drug selection and improve patient care through personalized treatment plans.

Support from Konfuzio
Konfuzio uses advanced AI technologies to accurately extract medical data from documents. The values obtained from the data extractions can then be used for simulations of medical procedures, helping medical professionals to plan and carry out treatments more precisely.

Urban planning and smart city initiatives

Digital twins support urban planners by providing real-time 3D and 4D spatial data and integrating augmented reality systems to make urban development more efficient. They are closely linked to smart city efforts by merging geo-information into infrastructure and helping urban planners to implement more efficient and citizen-friendly measures.

Support from Konfuzio
Konfuzio offers software solutions that process urban data and for subsequent visualization with third-party systems in order to optimize planning processes and improve citizen participation by providing transparent information.

Aviation

Digital twins are used in the aviation industry to reduce development times and production costs. They enable precise simulations and analyses of turbomachinery production, which can increase efficiency and avoid costly manufacturing errors. Digital twins also support the ongoing maintenance and optimization of aircraft components throughout their entire life cycle.

Support from Konfuzio
Konfuzio offers advanced AI-powered solutions for the aviation industry that help to monitor and optimize manufacturing processes. The analysis of real-time data offers more effective planning of maintenance cycles and early identification of potential faults, which improves operational safety and efficiency.

Automotive industry

In the automotive industry, digital twins enable in-depth analysis and optimization of vehicle design and manufacturing processes. They offer manufacturers the opportunity to simulate vehicle performance and increase efficiency in the production process even before physical prototypes are created. This reduces development costs and accelerates the time-to-market of new models by enabling the twin system to make accurate predictions about performance characteristics and potential manufacturing challenges.

Support from Konfuzio
Konfuzio specializes in extracting valuable insights from the large amounts of data generated by a digital twin in the automotive industry. Konfuzio's AI-powered software can analyze and interpret data from different phases of the vehicle lifecycle to identify patterns and trends that are critical to product development and quality control. This enables continuous improvement of product quality and process efficiency through data-driven decisions.

Digital twins vs. simulations

Both simulations and digital twins use digital models to replicate system processes, but the digital twin provides more extensive information for research purposes. The main difference between a simulation and a digital twin lies in their scope of performance. While simulations typically analyze single processes, a digital twin can simulate multiple processes simultaneously. In addition, simulations do not normally rely on real-time data, whereas digital twins use a bidirectional flow of information: They receive real-time data from sensors on the real object, which a system processor processes and sends its findings back to the object. With access to current data from different areas and the additional computing capacity of a virtual environment, digital twins can analyze more comprehensive problems and offer greater potential for improving products and processes.

Conclusion and outlook - digital twin in the future

Companies are currently experiencing a fundamental transformation in asset-intensive sectors, driven by digital innovation. Digital twins play a central role in this by providing an integrated view of physical and digital aspects of assets and processes and continuously expanding their capabilities to optimize product and process efficiency. The market for digital twins is being driven significantly by the growth of IoT and cloud technologies. These optimize production processes, lower costs, improve maintenance procedures and reduce downtime. Major technology leaders such as Microsoft, Google and Amazon Web Services have developed innovative digital twin functions that enable the development of new networked products and promote close integration between the digital and physical worlds. The Internet of Things is expanding the possible uses of digital twins from industrial applications to smart city initiatives.

Konfuzio complements these technologies with advanced AI software that enables the analysis of real-time data and helps companies to optimize their monitoring and maintenance processes. The integration of Konfuzio into digital twin systems enables companies to take full advantage of their data and optimize the value chain end-to-end.