Simulations & Virtual Reality, Digital Twins and Augmented Reality Supporting Automation and Production Solutions (pt 2)
AAE (Grauel) pushes technical boundaries. We provide high tech printing & assembly solutions. We support smart printing and manufacturing equipment with Industry 4.0 technologies and solutions. This series of articles provide background information on how these developments are supported.
In the previous article we looked at simulations and how they support the evaluation and development of machines and processes. Simulations are a great tool and may be very realistic but do not include real-time data (from the outside world). Adding this information creates a new dimension to support the development and evaluation of machines and processes.
This is where the digital twin comes in. In this article we will look at the history of the digital twin and the definition, this will be reviewed in the first paragraph. In the second paragraph we will take a “deeper look” on how to build a digital twin with a simplified example.
The last paragraphs look at the benefits for both the automation supplier and manufacturing companies in using digital twins.
Digital Twin, history and definition
Let’s start by reviewing the history and definition of a digital twin as it helps to understand its use and application. A digital twin was first introduced by John Vickers of NASA in 2002 and later used by Michael Grieves in 2002/2003. While Grieves’ was looking more specifically at product lifecycle management (which now relates to Industry 4.0), NASA’s interest in the Digital Twin was motivated by the requirement to operate, maintain and repair physical systems that are in space (which obviously are not available for testing and evaluation).
An overview of the history of the “digital twin” technology is shown below.
Our timeline starts in 1960 with a widely known digital twin that was built for Apollo 13. The main aspect for NASA was not to build an exact (replica) model but the identification, capturing and updating of the digital twin with data describing the condition of the physical asset (this is an important aspect of a digital twin).
This is where a clear definition is needed as there may be some confusion (or broad interpretation) over the definition. Frequently “models performing computer simulations” are defined as digital twins.
But looking at the definition a bit deeper, the paper “Demonstration of an industrial framework for an implementation of a process digital twin” provides a good definition (based on the definitions from Siemens and IBM). In this paper a digital twin is defined as:
A virtual mimic of a physical asset utilizing real-time data.
Note that a “virtual mimic” does not require a real-life model of an asset, even 2D models or sketches can provide a good virtual twin. The most important aspect of this definition (and difference with a simulation) is the inclusion of real-time data. The article also defines monitoring, optimization, prognostics and diagnostics as functionalities which also are important to consider.
Difference between Digital Twin and Simulations
The terms simulation and digital twin are often used to refer to the same object. But both concepts hold a different definition. Both definitions are included below to illustrate the difference between the two.
Simulations: refer to digital models that imitate the operations or processes within a system. Such simulations are used for analyzing the performances of systems and the testing and implementation of new ideas. Simulations are run using computer-aided design software applications.
Digital Twin: is the digital representation of physical or non-physical processes, systems, or objects. The digital twin also integrates all data produced or associated with the process or system it mirrors. Thus, it enables the transfer of data within its digital ecosystem, mirroring the data transfer that occurs in the real world. The data used in digital twins are generally collected from Internet of Things devices, edge hardware, HMIs, sensors, and other embedded devices.
Very simply put, a digital twin adds or interacts with data from the actual environment, a digital twin bridges the physical and digital reality.
The image below shows the actual robot and the model(s). The next step is to have the model receive data and behave like its real live-life counterpart so it can be programmed and tested before the software is implemented with the actual robot.
Digital Twin, a deeper look
In this paragraph the steps needed to build a digital twin are described. We will look at specific software where alternative solutions are available.
The first step in creating a digital twin is to create a model or import a CAD model*1. A CAD model is not directly suitable to be used, as the model needs to be optimized to reduce processing time for visualization.
*1 it is recommended to define the functions first (e.g., through Unified Modeling Language) but we skip this step here.
Most software packages that are used to create digital twins need triangulated polygon surfaces (CAD models use sets of adjustable mathematical parameters to define parametric solids not triangulated polygon surfaces). To prepare and optimize a model for real time “behavior” and interaction, a process of “tessellation” is used. This is a process that requires the removal of unnecessary data that is not required to represent the 3D model (and consumes valuable processing time).
Some suppliers provide tessellated models but when these are not available, the tessellation process can also be done manually (e.g., Blender, Maya, 3D max) or more automated (e.g., PiXYZ).
For illustration purposes we include a representation of a robot (taken from a CAD system, shown on the right) next to a lightweight version (the light weight is included for illustration purposes only, the true representation includes surfaces as well) which is more optimized to be used as a digital twin.
Adding more details, behavior and logic
Now that we have the model available, we need to tell the software what the components are and how they behave and interact. The various components (actuators, motors, drives, etc) need to be identified and classified. This allows for actual and real–world behavior of the (tessellated) model.
An example is included below showing the different parts of a robot. Each individual part will be assigned a “behavior”.
For each component, the behavior needs to be defined so it can be programmed and used. An example of the various joints is shown in the image, this is the native Unity solutions (alternative and more accurate solution are available through 3rd party software packages).
Once we have defined the signals and interfaces, we can now connect a PLC to the model. After that we can start programming the PLC software (e.g., Siemens, Beckhoff, etc.). The PLC interface takes the data from the PLC and interfaces with the model.
The model will now act (and react) on the signals and behave as the real-world model. Signals generated by the models can be provided back for testing. The interfacing with the PLC is shown below (the code is included for reference purposes where custom signals can be programmed as well).
Since the model behaves just as the real–world model, the (PLC) software can be written and tested without the need for a physical part. We can also feed signals from an existing application back into the model to understand behavior and start making predictions.
Digital Twins Supporting Automation Decision
A virtual twin bridges the gap between design and the use of a system. A digital twin behaves identical to the real-world system and allows for additional virtual testing and feedback before it is applied in the real world.
With the support of a digital twin, the development and testing of software can start in an early stage (e.g., in the design phase). The behavior and interaction with adjacent processes can be tested and optimized. Once the complete digital twin is available it can be used for testing or to perform a virtual FAT or virtual commissioning.
Additional virtual testing using a digital twin can be used for detecting interference among various components of the equipment, assessing ergonomics, and predicting equipment behavior under a variety of environments and situations. The digital development twin also allows for better safety testing and validation with direct feedback to engineering providing a risk–free testing environment.
Other benefits that digital twins provide (and are shown in various studies) include faster time to market new products and projects, first time right engineering and clear communication between various departments. Some studies show that savings up to 25% have been realized with the use of digital twins as development costs are reduced.
Digital Twins Supporting Manufacturing Decision
Once the real machine has been introduced to the manufacturing company, the digital twin can be used to support manufacturing companies in various ways.
Collaboration between different teams in the manufacturing floor can be eased with a digital twin in place. All parties can observe the outcome of changes understanding the impact on production and quality. Specifications of the equipment can be clarified with various component suppliers, so that the final design can be optimized for manufacturing.
Already in the design phase of the equipment (at the automation company) can the manufacturing company test and optimize the production line with respect to its layout, material flows, and processes.
Installation verification can be further supported by Augmented Reality (showing the machine on the actual production location). Once installed the digital twin can be used to accumulate data related to the machine’s performance and operating conditions.
The data acquired can be used to generate insights related to the usage trends of the equipment. Updates for the machine can be tested and verified using the digital twin, in a risk–free environment, prior to actual changing the parts or software. The equipment manufacturer can support the production company with the testing and validation of the machine.
Stay tuned for more, coming up next month! In the meantime, please consider following Grauel, a brand of AAE, on LinkedIn for weekly updates and extra content.
‘Simulations & Virtual Reality, Ditigal Twins and Augmented Reality Supporting Automation and Production Solutions’, by Ivo Brouwer – Business Developer Production Automation at AAE b.v.
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Diagram ‘Digital Twin’ Technology over the Years: https://www.futurebridge.com/industry/perspectives-mobility/application-of-digital-twin-in-industrial-manufacturing/