Misconception about digital twins

Digital twin technology has gained significant attention in recent years as it promises to revolutionize the way we design, manufacture, and maintain industrial systems. In the field of industrial production, many people still have a wrong understanding of the digital twin concept.

Because media visuals show digital twin pictures with CAD models near physical objects, a significant number of engineers think that these CAD models, with simulations associated, are digital twins. However, this is a common misconception. In reality, digital twin technology goes much beyond a mere visualization tool and involves the integration of various technologies such as sensors, IoT, data analytics, and machine learning to create a virtual replica of a physical system. A 3D CAD model is essentially a digital representation of the physical geometry of a product or system. It can be used to create visualizations, perform simulations, and generate manufacturing instructions. However, it is only a representation of the physical object, and does not provide any insight into the behavior or performance of the object in real-world conditions.

A digital twin is a virtual replica of a physical system that is created using a combination of sensor data, historical data, and physics-based models. This virtual replica can be used to simulate the behavior of the physical system in real-time and predict its future performance. The digital twin is not only a visual representation of the physical system, but it also includes a dynamic data model that is updated in real-time based on the inputs from various sensors. One of the key advantages of digital twin technology is that it allows for predictive maintenance of industrial systems. By monitoring the performance of a physical system through its digital twin, engineers can identify potential issues before they become critical and take corrective action to prevent downtime or costly repairs. Digital twin technology can also be used to optimize the performance of industrial systems by simulating different operating scenarios and identifying the most efficient operating parameters.

Another misconception about digital twin technology is that it is limited to manufacturing and industrial applications. In reality, digital twin technology can be applied to a wide range of systems, including buildings, transportation systems, and even entire cities. For example, a digital twin of a city can be used to simulate traffic flow and optimize transportation routes, leading to reduced congestion and improved air quality.

To create a digital twin, a combination of hardware and software is required. Sensors are used to collect data on the physical system, while IoT devices are used to transmit this data to the cloud for processing. Data analytics and machine learning algorithms are then used to create a dynamic data model that can be used to simulate the behavior of the physical system. The digital twin can be visualized using 3D CAD models or other visualization tools.

For examples, simulations of robotized factories in Delmia, Process Simulate, or other tools, such as RoboDK, RobotStudio, Visual Components are considered by some people as digital twins of the physical factory. This is wrong and in this post the intention is to remove this erroneous conception. While 3D CAD models can be useful in creating a digital twin, they are only a small part of the overall picture. In order to create an effective digital twin, it is necessary to incorporate data from a variety of sources, including sensors, simulation models, and historical data. This data can be used to create a dynamic model of the system that can be used to monitor its performance and identify potential issues before they become critical.

So, while a 3D CAD model can be part of a digital twin, it is not the same thing. A digital twin is a virtual representation of a physical system that incorporates data from sensors, real-time operating conditions, and other sources to simulate the behavior and performance of the physical system. It is a dynamic model that can be used for various purposes, such as predictive maintenance, optimization, and simulation.

On the other hand, a 3D CAD model is a static digital representation of a physical object or system, and sometimes a quasi-kinematic representation. It provides a visual representation of the geometry and structure of the object but does not include any information about its real-time operating conditions or performance. While a 3D CAD model can be useful for design and visualization purposes, it cannot provide the same level of insight and analysis as a digital twin.

So, what is a digital twin?

A digital twin is a virtual representation of a physical asset or system, which can be used for monitoring, analysis, and optimization purposes. In the year 2002, Dr. Michael Grieves spoke about digital twin at a conference held at Michigan University and presented the digital twin concept but it was presented as “Conceptual Ideal for PLM”. The architecture of a digital twin typically involves several components, as outlined below:

1. Physical Asset: The physical asset is the real-world object that the digital twin represents. This could be anything from a manufacturing machine to a wind turbine to a building.
2. Sensors: The sensors are used to collect data from the physical asset. This data can include information about the asset’s performance, condition, and environment. The sensors can be embedded within the asset or external to it, depending on the application. They can be used to measure temperature, pressure, humidity, stress, vibration, and other parameters.
3. Actuators: These are devices that control the physical object or system. They can be used to change the position, speed, or direction of movement of the object or system. They might be part of some systems, but not in all cases.
4. Communication Network: This component is responsible for transmitting data from the physical object or system to the digital twin. It may include wired and wireless networks, such as Ethernet, Wi-Fi, or cellular networks.
5. IoT Gateway: The IoT gateway is used to collect and transmit data from the sensors to the digital twin. Thay are present in some communication architectures where IoT is needed. This can be a physical device or a cloud-based service that aggregates and processes the data.
6. Data Acquisition System: This component is responsible for collecting data from sensors and other sources. It can include data loggers, programmable logic controllers (PLCs), and other devices.
7. Data Storage and Management: This component is responsible for storing and managing the data collected from the physical object or system. It may include databases, data warehouses, and other storage systems. For example, the data collected from the sensors is stored in a database or data lake. This data can be accessed and analyzed by various applications.
8. Models of the Physical Units: This part of the architecture contains various models of the asset, from static models, to kinematic, and dynamic models. For each relevant component of the asset there are various mathematical formalisms for modelling behaviour. This means, a given module can have several models, thus several digital twins.
9. Analytics Engine: The analytics engine is used to process the data collected from the sensors and generate insights about the physical asset. This can include predicting when maintenance is required, identifying opportunities for optimization, and detecting anomalies.
10. Visualization and User Interface: The visualization and user interface component provides a way for users to interact with the digital twin. This can include dashboards, reports, and other tools that enable users to view and analyze the data collected from the physical asset.
11. Machine Learning and AI: Machine learning and AI technologies can be used to improve the performance of the digital twin. This can include using predictive models to forecast future behavior, using natural language processing to enable conversational interfaces, and using image recognition to identify anomalies.
12. Cloud Computing Platform: This component provides the computing resources needed to run the digital twin. It may include cloud services such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform.
13. Security and Access Control: These components are responsible for ensuring the security and privacy of the data collected from the physical object or system. They can be used to authenticate users, encrypt data, and monitor access to the system.

In this architecture, the real-world system is monitored by sensors, which generate sensor data. This sensor data is then processed by a data pipeline, which may include filtering, preprocessing, and aggregation steps. The resulting data is then used to create a digital replica of the real-world system.

The digital replica includes a control system that can use the digital twin to simulate the behavior of the real-world system, make predictions, and optimize performance. The simulation component of the digital twin can be used to test changes or improvements to the real-world system before implementing them. In this respect, the digital replica must be controlled by a virtual controller that replicates the same rules and behaviours as the physical counterpart.

For example, RobotStudio from ABB has a graphical component for representing 3D models of robotic cells, but to make a step forward towards a digital twin it has the possibility to link the CAD model to a virtual controller that uses the same operating system as the real robot (RobotWare), it can replicates the teach pendant, and uses the RAPID programming language, integrates signals and smart components, as well as links to PLCs. It includes the kinematic models of the robots, too, but it still misses the dynamic model of the mechanical structure, of the transmission system, of the driving system and of the control system. So, this provides only a part of what a digital twin must be.

A block scheme for a digital twin of a CNC machine-tool is shown below.

In this block diagram, the digital twin of the CNC machine-tool is represented as a separate unit that interfaces with the physical CNC machine-tool and the cloud platform. The digital twin consists of three main modules:

1. Data Ingestion and Processing Module: This module is responsible for collecting and processing data from the physical CNC machine-tool, such as sensor readings and machine status. The data is then sent to the cloud platform for analysis.
2. Simulation Module: This module creates a virtual model of the CNC machine-tool that can be used to simulate various scenarios, such as changes to machine settings or tool paths. The simulation results can be used to optimize machine performance and identify potential issues before they occur.
3. Analytics and Visualization Module: This module provides real-time analytics and visualization of machine data, allowing operators to monitor machine performance and identify trends or anomalies. It also provides tools for predictive maintenance and process optimization.

The physical CNC machine-tool is connected to the digital twin through sensors and actuators, which feed data into the Data Ingestion and Processing Module. The digital twin also interfaces with the cloud platform, which provides additional analytics and visualization capabilities as well as storage and data processing resources.

Digital twin is bidirectional with the physical twin. It can receive real-time data from the physical twin and use it to update its digital representation. Similarly, it can also send commands and instructions back to the physical twin, allowing for control and optimization of its operation. Below we have a graphical representation of the communication flow.

In this diagram, the physical twin is represented by sensors and actuators that communicate with a Programmable Logic Controller (PLC) over industrial communication protocols such as Modbus or EtherNet/IP. The PLC is responsible for controlling the physical twin and communicating with the digital twin.

The digital twin is represented by virtual sensors that receive data from the physical twin via the OPC UA protocol, which is used for machine-to-machine communication. The virtual sensors store this data in a data store. The digital twin also communicates with the cloud platform using MQTT, which is a lightweight messaging protocol designed for IoT applications.

The cloud platform provides a central location for storing and analyzing data from the digital twin. It may also provide tools for visualization, analytics, and machine learning. The communication between the digital twin and the cloud platform is also done using MQTT.

MQTT (Message Queuing Telemetry Transport) is a lightweight publish/subscribe messaging protocol designed for constrained devices and low-bandwidth, high-latency, or unreliable networks. It is commonly used in IoT and M2M applications due to its low overhead and efficient data transfer capabilities. The following is a detailed block diagram of the MQTT protocol:

In the above diagram, the MQTT protocol allows a client (e.g. an IoT device) to publish messages to a broker/server, which then distributes the messages to any subscribed clients. The communication happens over TCP/IP and uses a publish/subscribe messaging model.

The MQTT protocol has the following components:

1. MQTT Client: A device or application that publishes or subscribes to MQTT messages.
2. MQTT Broker (Server): A server that receives and distributes MQTT messages between clients.
3. Topics: Messages in MQTT are organized around topics. A topic is a string that identifies a particular type of message. For example, a temperature sensor might publish messages to the topic “sensors/temperature”.
4. Messages: MQTT messages consist of a topic and a payload, which can be any arbitrary data.

The communication flow in MQTT follows the following steps:

1. The MQTT client establishes a TCP connection to the MQTT broker.
2. The client subscribes to one or more topics on the broker.
3. Another MQTT client publishes a message to a topic on the broker.
4. The broker receives the message and forwards it to all clients subscribed to that topic.
5. The client that subscribed to that topic receives the message and takes appropriate action.

The MQTT protocol uses a binary message format that is designed to be compact and efficient. It supports three quality of service levels (QoS) to provide reliability and ensure delivery of messages.

The software part of the digital twin typically consists of multiple layers, including the perception layer, communication layer, data layer, analysis layer, and application layer.

1. Perception layer: This layer includes sensors, actuators, and other devices that interact with the physical world. The sensors and actuators gather data from the physical world and send it to the communication layer.
2. Communication layer: This layer manages the communication between the perception layer and the rest of the digital twin. It typically includes a variety of protocols, such as MQTT, CoAP, and OPC UA, to enable communication between the sensors and the digital twin.
3. Data layer: This layer stores and manages the data generated by the digital twin. It includes a variety of databases, such as SQL and NoSQL databases, and data storage technologies, such as Hadoop and Spark, to store and process the data.
4. Analysis layer: This layer is responsible for processing and analyzing the data generated by the digital twin. It includes a variety of tools and algorithms, such as machine learning algorithms and statistical analysis tools, to extract insights from the data.
5. Application layer: This layer includes the applications that use the data generated by the digital twin. It includes applications for monitoring and controlling the physical system, as well as applications for optimizing and improving the system.

A detailed block diagram for the software part of a digital twin is shown below. This diagram shows the major software components of a digital twin, including data acquisition, data processing, data analytics, sensor interface, PLC interface, network communication, MQTT broker, MQTT clients, data storage, user interface, and analytics platform. The diagram also shows the communication links and protocols used between these components.

The flow in the software part of a digital twin typically involves the following steps:

1. Data acquisition: This component is responsible for gathering data from various sensors and data sources, such as PLCs, SCADA systems, and other equipment. The data is then transmitted to the digital twin for further processing.
2. Data processing: The raw data is processed to filter, clean, and transform it into meaningful information. This process typically involves data normalization, data aggregation, and data enrichment.
3. Data analytics: Once the data has been processed, it can be analyzed to identify patterns, trends, and anomalies. This analysis can help identify potential issues and predict future behavior.
4. Sensor interface: The sensor interface component enables communication with the physical sensors and devices in the system. This component must be able to handle different types of sensors and communication protocols.
5. PLC interface: The PLC interface component enables communication with the programmable logic controllers (PLCs) that control the physical equipment. This component must be able to read and write data to the PLCs and interpret the PLC logic.
6. Network communication: The digital twin must be able to communicate with other systems and devices on the network. This can be achieved using different protocols, such as MQTT, OPC UA, and Modbus.
7. MQTT broker: The MQTT broker acts as a central hub for communication between the digital twin and other MQTT clients. It receives and routes messages based on the topic hierarchy and subscriber rules.
8. MQTT clients: The MQTT clients are responsible for sending and receiving messages to and from the digital twin. These clients can be other software systems or physical devices, such as sensors or PLCs.
9. Data storage: The data storage component is responsible for storing the processed and analyzed data. This can be done using different types of databases, such as SQL, NoSQL, or time-series databases.
10. User interface: The user interface component enables users to interact with the digital twin and view the data and analytics. This can be done using different types of interfaces, such as dashboards, reports, or custom applications.
11. Analytics platform: The analytics platform component provides additional analytics and visualization tools to analyze the data and identify insights. This can include machine learning models, predictive analytics, and anomaly detection algorithms.

To recapitulate, we have:

• Data acquisition: Communicates with sensors and other data sources to collect raw data from the physical system.
• Sensor interface: Communicates with the data acquisition component to receive raw sensor data.
• PLC interface: Communicates with the Programmable Logic Controller (PLC) to receive information about the state of the physical system and send commands to control it.
• Data processing: Receives raw data from the data acquisition component and processes it into a more usable format.
• Data analytics: Analyzes the processed data to extract useful insights and generate predictions about the behavior of the physical system.
• MQTT broker: Acts as a central hub for all MQTT communication between the different components.
• MQTT clients: Send and receive data via the MQTT broker. For example, the data processing component may publish processed data to the MQTT broker, and the data analytics component may subscribe to this data to perform its analysis.
• Data storage: Stores both raw and processed data for future use.
• User interface: Provides a graphical interface for users to interact with the digital twin, view data, and control the physical system.
• Analytics platform: Provides advanced analytics capabilities, such as machine learning and artificial intelligence, to enhance the predictive capabilities of the digital twin.

A breakdown of each component of the digital twin with examples of real technologies is detailed below:

• Data Acquisition: This component involves collecting data from the physical system through various sensors and devices. The data can be in the form of temperature, pressure, speed, vibration, and more. Examples of technologies for data acquisition include sensors like accelerometers, strain gauges, and thermocouples.
• Data Pre-processing: Once the data is collected, it needs to be pre-processed before it can be used for analysis. This can involve filtering, normalization, and transformation of the data. Technologies used for data pre-processing include MATLAB and Python libraries like Pandas and NumPy.
• Data Storage: The pre-processed data needs to be stored in a database or data warehouse. Technologies used for data storage include SQL and NoSQL databases like MySQL, MongoDB, and Cassandra.
• Analytics: The stored data is then analyzed using various techniques like machine learning, statistical analysis, and data visualization. Examples of analytics technologies include Python libraries like Scikit-learn and TensorFlow, and visualization tools like Tableau and PowerBI.
• Simulation: The digital twin can be used to simulate the behavior of the physical system under various conditions. Technologies used for simulation include:
  • Finite element analysis (FEA) software like ANSYS and Abaqus,
  • Computational fluid dynamics (CFD) software like OpenFOAM and ANSYS Fluent,
  • COMSOL Multiphysics: a simulation software used for multiphysics modeling and simulation. It allows for the simulation of physical systems that involve multiple physical phenomena, such as heat transfer, fluid flow, and structural mechanics,
  • MATLAB: a high-level programming language and numerical computing environment used for simulation and modeling in various industries. It offers a range of tools for data analysis, visualization, and modeling,
  • Simulink: a simulation and modeling software widely used in control engineering and other industries. It offers a graphical interface for building models of physical systems, and can be used for simulating dynamic systems, such as those found in robotics, aerospace, and automotive engineering,
  • AnyLogic: a simulation and modeling software used for a wide range of applications, including digital twins for manufacturing, logistics, and transportation,
  • Dassault Systèmes SIMULIA: a suite of software tools for simulating and analyzing the behavior of materials, structures, and fluids,
  • PTC Creo Simulation Live: A real-time simulation tool integrated with the CAD software PTC Creo, allowing engineers to see the impact of design changes on performance in real-time etc.
• Optimization: Based on the simulation results, the digital twin can be used to optimize the performance of the physical system. Technologies used for optimization include genetic algorithms, particle swarm optimization, and simulated annealing.
• Control: The digital twin can also be used for control and decision-making purposes. Technologies used for control include model predictive control (MPC) and proportional-integral-derivative (PID) controllers.

• Visualization: Finally, the digital twin can be visualized using 3D modeling and virtual reality technologies. Examples of visualization technologies include Unity3D, Unreal Engine, and Blender.

Data processing is a crucial component in any digital twin system. It involves the collection, cleaning, aggregation, analysis, and visualization of data from various sources, including sensors, machines, and other systems. This unit typically includes software tools and platforms such as Hadoop, Spark, and TensorFlow for big data processing, machine learning, and artificial intelligence tasks. It may also include traditional business intelligence tools like Tableau and Power BI for data visualization and reporting.

The data processing unit is responsible for performing various tasks such as data cleaning, data transformation, and data analysis. Data cleaning involves identifying and correcting errors and inconsistencies in the data, while data transformation involves converting data from one format to another. Data analysis involves applying statistical and analytical techniques to the data to identify trends, patterns, and insights.

In addition to these core functions, the data processing unit may also include a data storage component, which provides a repository for storing processed data. This data can be stored in a variety of formats including relational databases, NoSQL databases, and data lakes.

A digital twin of an industrial robot

A digital twin for an industrial robot is a virtual model that represents the physical robot and its behavior in real-time. It is a dynamic replica of the robot, created through the integration of hardware, software, and data. The digital twin captures real-time data from sensors and other sources, which is then processed to create a model that can be used to optimize performance, predict failures, and identify opportunities for improvement.

The hardware components of a digital twin for an industrial robot typically include sensors, actuators, and other devices that are used to capture and process data. These sensors and devices are typically integrated into the robot itself, and are used to monitor things like temperature, vibrations in joints and end-effector, actual positions in joints, actual speeds in joints, arm deflections, and other performance metrics. The data collected by these sensors is then transmitted to the software components of the digital twin, where it is processed and analyzed.

The software components of a digital twin for an industrial robot typically include a variety of tools and applications that are used to create, manage, and analyze the digital twin. These tools may include data analytics software, machine learning algorithms, and other tools for processing and visualizing data. The software may also include simulation tools that allow engineers and technicians to test and optimize the robot’s performance in a virtual environment before making changes to the physical robot.

In order to create an effective digital twin for an industrial robot, it is important to have a clear understanding of the robot’s capabilities and behavior. This requires detailed knowledge of the robot’s mechanical and electrical components, as well as its programming and control systems. In addition, it is important to have access to real-time data from the robot, which can be used to continuously improve the accuracy and reliability of the digital twin.

The relationship diagram for a digital twin for an industrial robot is complex and multi-faceted. At the center of the diagram is the physical robot, which is connected to a network of sensors, actuators, and other devices that collect data on the robot’s performance. This data is transmitted to the software components of the digital twin, where it is processed and analyzed to create a model of the robot’s behavior.

The digital twin is then used to optimize the robot’s performance and predict failures, allowing engineers and technicians to identify opportunities for improvement and proactively address issues before they lead to downtime or other problems. The digital twin may also be used to simulate the robot’s behavior in different environments or scenarios, allowing engineers to test and refine the robot’s performance before making changes to the physical robot.

There are several categories of digital twins, but for an industrial robot, some of the relevant categories include:

The inverse direction: the closed-loop control

Economic benefit from digital twins

Selling digital twin projects to managers of manufacturing companies

Here are some key points you could use to approach a manager of a manufacturing company about investing in digital twins:

• Start with the benefits: Emphasize that a digital twin can help the company reduce downtime, increase productivity, and improve quality control. This will lead to cost savings, increased revenue, and improved customer satisfaction.
• Highlight the competitive advantage: Explain that implementing digital twins can help the company stay ahead of competitors who have not yet adopted this technology. With digital twins, the company can optimize its operations, make more informed decisions, and ultimately improve its bottom line.
• Showcase success stories: Provide examples of other companies in the same industry that have successfully implemented digital twins and seen significant improvements in their operations. Use real-world examples to illustrate the tangible benefits of this technology.
• Address potential concerns: Acknowledge that investing in digital twins requires an upfront cost, but explain that the long-term benefits far outweigh the initial investment. Also, address any concerns the manager may have about the complexity of implementing digital twins and the need for additional technical expertise.
• Offer a plan: Outline a plan for implementing digital twins, including the hardware and software required, the estimated cost, and the timeline for implementation. Provide a detailed breakdown of the costs and benefits, including the ROI and payback period.
• Show your expertise: Highlight your experience in implementing digital twins for other companies, and provide case studies or testimonials to back up your claims.

It is also important to communicate the potential benefits of digital twins in a clear, concise way and to address any concerns or objections the manager may have. By showing that digital twins can provide a significant ROI and competitive advantage, you can make a compelling case for investing in this technology.

Here is a step-by-step approach to prepare a presentation to convince a manufacturing company manager to invest in digital twins:

• Understand their pain points: Before presenting the benefits of digital twins, it’s important to understand the current challenges the manager is facing. This could be related to production efficiency, maintenance costs, downtime, quality issues, or any other relevant aspect.
• Explain what digital twins are: Begin the presentation by explaining what digital twins are and how they work. Use clear, concise language and avoid technical jargon as much as possible. You could use images or videos to help illustrate the concept.
• Highlight the benefits: Once the manager understands the concept of digital twins, highlight the potential benefits they could provide to their organization. This could include increased production efficiency, reduced downtime, better predictive maintenance, improved product quality, and more.
• Provide case studies: Share case studies or success stories of other manufacturing companies that have implemented digital twins and the results they have achieved. This can help build credibility and demonstrate the real-world benefits.
• Address concerns: It’s possible that the manager may have concerns about the cost, complexity, or implementation of digital twins. Be prepared to address these concerns and provide solutions or alternatives if necessary.
• Present a proposal: Finally, present a proposal that outlines the specific benefits and costs of implementing digital twins in their organization. This should include a cost-benefit analysis, ROI calculations, and a clear implementation plan.

It is necessary to tailor the presentation to the specific needs and concerns of the manager and their organization. It’s important to be persuasive, but also transparent and realistic about the potential benefits and costs of investing in digital twins.

Use case

We will consider at the end of this material a brief description of a digital twin based on affordable technologies. In this respect, we consider RoboDK for robot modeling and simulation, Python and RoboDK API to interface with Python, the capacity of RoboDK to create postprocessor, nodeRED as middleware, Python library snap7 to work with Siemens PLCs, and a Siemens PLC, as well as a cloud platform.

Here is an example of how you could set up a digital twin of an industrial robot using RoboDK, Python, nodeRED, Siemens PLC, and cloud resources:

• Design the robot in RoboDK:
  • a. Start by designing the robot in RoboDK’s 3D simulation environment. Use the drag-and-drop interface to build the robot, create movements, and define the robot’s parameters such as reach, payload, and speed.
  • b. RoboDK allows you to create a digital twin of the robot by defining its kinematic and in combination with Python to introduce the dynamic models, too (e.g., using Euler-Lagrange formalism). We can also add the models of the robot’s joints, motors, sensors, and actuators by means of Python coding and integration of various formalisms (e.g., Laplace transfer function, etc.).
• Connect to the robot using a Siemens PLC:
  • a. Using a Siemens PLC, you can interface with the robot and control its movements and operations.
  • b. You can use Python and the snap7 library to program the PLC and communicate with the robot.
• Collect data using nodeRED:
  • a. nodeRED is an open-source tool that allows you to create flows for IoT devices and other systems.
  • b. You can use nodeRED to collect data from the robot and its sensors, such as motor temperature, vibration, and power consumption.
• Analyze and store data using cloud resources:
  • a. You can use cloud resources such as AWS or Google Cloud to store and analyze the data collected from the robot.
  • b. Python can be used to analyze the data and create insights for predictive maintenance, fault diagnosis, and other applications.

A digital twin of an industrial robot can be created using RoboDK, a simulation and offline programming software for industrial robots. The digital twin can be connected to a real industrial robot through a Siemens PLC using the snap7 library in Python. The digital twin can also be connected to cloud resources such as Azure IoT Hub or AWS IoT Core for remote monitoring and control.

The RoboDK software can be used to create a virtual model of the industrial robot and simulate its movements and operations. The model can be programmed to perform specific tasks and optimized for efficiency. The Python programming language can be used to interact with the model, retrieve data from sensors, and send commands to the model or the real robot.

The snap7 library in Python can be used to communicate with the Siemens PLC that controls the real industrial robot. The library allows the digital twin to receive real-time data from the sensors on the robot and send commands to control the robot’s movements and operations.

Node-RED, a flow-based programming tool, can be used to connect the digital twin to cloud resources such as Azure IoT Hub or AWS IoT Core. This allows the digital twin to send data to the cloud for remote monitoring and control, and receive commands from the cloud to adjust its operations.

pip install python-snap7

data = plc.read_area(snap7.types.Areas.DB, 100, 0, 10)
print(data)

from robolink import *
from robodk import *
RDK = Robolink()
# create a new robot
robot = RDK.AddRobot('My Robot')
# set the robot parameters
robot.setJoints([0, 0, 0, 0, 0, 0])
robot.setPoseFrame(MakePoseTrans([0, 0, 0], [0, 0, 0, 1]))
robot.setTool(MakePoseTrans([0, 0, 100], [0, 0, 0, 1]))
# create a new program
prog = RDK.AddProgram('My Program', robot)
# add a move joint instruction
prog.MoveJ([0, 0, 0, 0, 0, 0])
# simulate the robot movement
sim = RDK.Simulation()
sim.setRunMode(SimulationRunMode.Simulating)
sim.setCollisionActive(True)
sim.setAccuracyActive(True)
sim.setSpeed(100)
# run the simulation
sim.Start()

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credits: Stelian Brad