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Industry 4.0 Glossary

What Is A Digital Twin?

A “Digital Twin” is a replication of the form and function of a physical entity in the digital environment using data. It includes all information about a particular asset and its ability to interact with or respond to other assets. In manufacturing, assets are the machines that are involved in production on the factory floor.

A digital twin makes it possible to simulate, study and predict scenarios that the physical asset may be subjected to. The digital twin has many different applications in the manufacturing industry. One example would be study of the performance of a jet engine (the product) in turbulent conditions to set the metrics for optimal production. For process factories like food manufacturers, the digital twin can be used to replicate the entire plant and all of its processes.

How Does A Digital Twin Work?

A digital twin is created by gathering data from a real-world physical thing or system through sensors. This data is used to create online models or replicas of the real-world system. With the help of the virtual digital replica, one can vary input parameters or stimuli to predict, observe and learn from different scenarios.

The four key steps to achieve this are sense – simulate – study – stimulate.

Sense

The first step is to identify all possible data points that define the attributes and behavior of an asset and gather this information using sensors. The evolution of IoT sensors has made it economically feasible to gather and transmit information across a wide variety of applications.

Simulate

The data gathered is subjected to analysis to build mathematical models that define these attributes and behavior. big data, data science, data mining and data analytics play a key role in building the models. The increase of computing power and sophisticated data handling software solutions have made developing complex non-linear models possible. These mathematical models will simulate the physical assets in a virtual environment.

Study

The digital replica through a mathematical model puts the power of analyzing different scenarios in the hands of professionals. They use the models to study response to changes in inputs, material properties, time, operating conditions and a wide variety of influencing parameters. This study helps in comprehending current outcomes, predicting future behavior and establishing safe operating limits. In some cases it can also identify a deliberate yielding point.

Stimulate

Once the scenarios are established, it can be taken and applied to the physical asset that is modelled by the digital twin. The benefit of this application is the elimination of waste by enabling the planning of resources, timed outcomes and increased outcomes. It also eliminates risks and uncertainties by decreasing failures and even reducing fatalities.
The accuracy of practical applications of stimuli based on the simulations of the digital twin depends on the accuracy of the modelling and the study. When we apply machine learning and artificial intelligence to the second and third steps of this process, it slowly course corrects the modelling engine as close to reality as possible.

How Digital Twin Is Different Than Digital Thread

A “Digital Thread” is an essential component to create a functional “Digital Twin” that can simulate a product’s journey from the drawing board to the salvage yard. The digital thread focuses only on the flow of data across the entire product lifecycle in a way in which it is trackable and can be studied at any given stage of the life of the product. A digital twin generates the information about the product and its behavior.

Digital Twin In Manufacturing

There are numerous use cases for a digital twin in a manufacturing enterprise. Depending on what is being modelled, the applications can vary. In a process factory that manufactures chemicals, the entire plant and its processes can be modelled and replicated to create a digital twin. Whereas in a rail engine yard, the product itself (the rail engine) can be modelled. But depending on what is being modelled, various benefits can be derived out of a digital twin.

Load balancing and throughput maximization is a very common use case for a digital twin. A digitally modelled manufacturing plant can be simulated for various inputs to increase throughput. The outcomes can then be used to deliver maximum output from the plant. This has several measurable monetary benefits like increased production, yield per square feet, asset utilization, energy efficiency, and TAKT time.

A digital twin can also be used to define safe operating limits for a manufacturing plant. The digital replica can be simulated to various yielding points to identify the minimum viable safe operating conditions. This can be used to define safety procedures and policies. The direct monetary benefit of this can be reduced downtime due to lesser workplace incidents, and reduced cost of insurance. It can also drive other non-monetary benefits like improved employee safety, better working conditions, higher levels of employee satisfaction and higher levels of employee retention.

Simulation of a real-world entity in a digital environment can also help identify causes of issues in quality. This can be preemptively addressed in the production environment thereby improving quality, reducing waste, and reducing the cost of quality. Digital twins can also help in reducing uncertainty involved in a production line. This can contribute to business continuity and risk mitigation.

Digital Twins In Manufacturing

Digital twins have already permeated the manufacturing domain. Enterprises around the world are enjoying measurable and intangible benefits from the digital twin concept. IoT, Machine Learning, AI, Digital Twin and a plethora of other technologies have all come together today to help us build a manufacturing enterprise that could only have been a dream just a decade ago.

The second and third industrial revolutions required the setup of assembly lines and installing expensive control systems. These technologies are not as capital intensive. IoT, Internet and Cloud Infrastructure have created accessibility to this advanced technology. All manufacturing enterprises, big & small, can now harness these technologies for their success.