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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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:
- MQTT Client: A device or application that publishes or subscribes to MQTT messages.
- MQTT Broker (Server): A server that receives and distributes MQTT messages between clients.
- 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”.
- 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:
- The MQTT client establishes a TCP connection to the MQTT broker.
- The client subscribes to one or more topics on the broker.
- Another MQTT client publishes a message to a topic on the broker.
- The broker receives the message and forwards it to all clients subscribed to that topic.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- Physics-based digital twins – These digital twins use mathematical models to simulate the physical behavior of the robot. They require knowledge of the robot’s physical properties, such as its geometry, mass, and inertial properties, as well as its control algorithms.
- Data-driven digital twins – These digital twins are based on data collected from sensors on the robot and other sources. They use machine learning algorithms to identify patterns in the data and predict future behavior of the robot.
- Hybrid digital twins – These digital twins combine both physics-based and data-driven models to achieve more accurate predictions.
For an industrial robot, a digital twin could include the following components:
- Sensors – These could include encoders to measure joint positions, force/torque sensors to measure contact forces, and cameras or depth sensors for visual perception.
- Control algorithms – These could include the robot’s inverse kinematics and dynamics models, as well as motion planning and trajectory generation algorithms.
- Simulation software – This could include software for simulating the robot’s physical behavior based on its geometry, mass, and other physical properties.
- Data storage and processing – This could include a database for storing sensor data and machine learning algorithms.
For an industrial robot, the mathematical models used in the digital twin can include:
- Kinematic model: This model describes the relationship between the joint positions of the robot and its end-effector position and orientation in the workspace. It includes the Denavit-Hartenberg parameters, which describe the geometry and kinematics of the robot.
- Dynamic model: This model describes the forces and torques required to achieve a certain trajectory or motion. It includes parameters such as mass, inertia, and friction.
- Control model: This model describes how the robot is controlled and how it responds to external inputs. It includes the controller parameters, such as proportional, integral, and derivative gains.
- Thermal model: This model describes how heat is generated and dissipated in the robot, which can affect its performance and lifespan. It includes parameters such as ambient temperature, heat generated by motors, and cooling system effectiveness. The thermal model can be also used to simulate the heat generated by the robot’s motors, electronics, and other components, and to predict their temperature rise under different operating conditions.
- Fault model: This model describes the possible faults or failures that can occur in the robot, such as motor failure, sensor failure, or communication failure. It includes parameters such as probability of failure and time-to-failure.
- Driving system model: This model may be important for predicting the robot’s power consumption and optimizing its energy efficiency.
- The deformation model: This model can be used to predict the deflection of the robot’s structure under different loads and to optimize its structural design.
- Failure models: These models are used to predict when a component or system is likely to fail. They can be based on statistical analysis of historical failure data or physics-based models that take into account the stresses and strains on the system.
- Health models: These models are used to track the current health status of the system and identify any potential issues before they become critical. They can be based on sensor data or machine learning algorithms that analyze data from multiple sources.
- Diagnostic models: These models are used to diagnose the root cause of any issues that arise in the system. They can be based on fault tree analysis or other diagnostic techniques.
- Prognostic models: These models are used to predict the remaining useful life of the system or components based on their current health status and expected usage. They can be based on physics-based models or machine learning algorithms.
The various models used in a digital twin for an industrial robot can be used together for predictive maintenance. For example, the kinematic and dynamic models can be used to simulate the behavior of the robot during normal operations and identify potential issues that may arise. These models can also be used to predict wear and tear on the robot’s components and help schedule maintenance tasks accordingly. The thermal model can be used to predict how the temperature changes in the robot during operation, and identify hotspots or areas that are prone to overheating. This can help prevent damage to the robot and extend its lifespan. The fault model can be used to identify potential faults or failures in the robot’s components based on their performance and environmental conditions. This information can be used to schedule maintenance tasks or replacement of parts before they fail, reducing downtime and repair costs. The model of the driving system can also be used to predict wear and tear on the robot’s motors and drives, helping to schedule maintenance tasks or replacements as needed.
Predictive maintenance based on data and machine learning involves using historical data from the digital twin, as well as real-time data from sensors, to train machine learning algorithms to identify patterns and anomalies that can indicate potential equipment failures. In this approach, the digital twin is used to collect and store data from the physical system, which is then processed and analyzed using machine learning algorithms to detect patterns and predict potential failures. The models used in this approach include statistical models, neural networks, and other machine learning algorithms. The predictive maintenance process based on data and machine learning typically involves the following steps:
- Data collection: Historical data and real-time sensor data are collected from the digital twin.
- Data preparation: The data is cleaned, preprocessed, and transformed to a suitable format for machine learning algorithms.
- Feature engineering: Relevant features are extracted from the data to represent the behavior of the equipment.
- Model training: Machine learning algorithms are trained using historical data to learn the patterns and relationships in the data.
- Model validation: The trained model is validated using test data to ensure it can accurately predict equipment failures.
- Predictive maintenance: The trained model is used to predict potential equipment failures, and maintenance actions are taken to prevent these failures from occurring.
Some specific machine learning models commonly used in predictive maintenance of industrial robots include:
- Random Forest: This algorithm is often used to predict component failure based on features such as vibration data, temperature data, and operating conditions.
- Recurrent Neural Networks (RNNs): RNNs are used for time-series data analysis and can be used to predict the remaining useful life of a component based on its historical data.
- Support Vector Machines (SVMs): SVMs can be used to classify machine health status and detect anomalies in sensor data.
- Long Short-Term Memory (LSTM) networks: LSTM networks are used for time-series data analysis and can be used to predict the remaining useful life of a component based on its historical data, similar to RNNs.
- Convolutional Neural Networks (CNNs): CNNs are used for image analysis and can be used to detect defects in robot components based on images from cameras or sensors.
- Bayesian Networks: Bayesian Networks can be used for fault diagnosis by analyzing the relationships between different sensor data and identifying the most likely cause of a fault.
In addition to the machine learning techniques mentioned earlier, there are other statistical and data analysis methods that can also be used for predictive maintenance in industrial robots. One such method is regression analysis, which involves analyzing the relationship between variables to make predictions. For example, regression analysis can be used to predict the remaining useful life of a robot based on the historical data collected from sensors and other sources.
Time-series analysis is another method that can be used to predict maintenance needs. This method involves analyzing data collected over time to detect patterns and trends. By analyzing the historical data of a robot’s performance, time-series analysis can help identify potential maintenance needs before they cause major problems.
Anomaly detection is another useful method for predictive maintenance in industrial robots. This method involves identifying patterns or events that deviate from the norm. For example, if a robot suddenly starts performing poorly or producing unexpected results, anomaly detection can be used to flag the issue and trigger maintenance before it becomes a major problem.
Overall, a combination of machine learning techniques, statistical methods, and data analysis tools can be used to create a comprehensive predictive maintenance program for industrial robots. By analyzing data from sensors, historical performance data, and other sources, these methods can help identify potential issues and take proactive steps to address them before they cause significant downtime or damage.
The hardware needed for a digital twin of an industrial robot includes the robot itself, sensors to capture data about its performance and environment, and a computer or server to run the digital twin software.
The software needed for a digital twin of an industrial robot includes modeling and simulation software, data analytics and machine learning software, and visualization software. These software tools allow the digital twin to process data from sensors, create and run mathematical models, and provide insights into the performance and health of the robot. They may also include communication protocols for connecting the digital twin to other systems or devices.
The inverse direction: the closed-loop control
The other direction, from the digital twin to the real industrial robot, is also important and is known as closed-loop control. In closed-loop control, the digital twin is used to monitor the performance of the physical system and provide feedback to control its behavior. This is achieved through the use of sensors that measure the physical system’s output, which is then compared to the desired output as modeled in the digital twin. The difference between the two is used to adjust the system’s inputs to achieve the desired output.
Various communication protocols and technologies are used to facilitate this closed-loop control between the digital twin and the physical system. For example, the industrial robot may be connected to the digital twin through an industrial Ethernet network, using protocols such as EtherCAT, PROFINET, or Ethernet/IP. The digital twin may also be connected to a programmable logic controller (PLC) or other control systems using protocols such as Modbus or OPC UA.
This closed-loop control is particularly relevant in applications such as real-time control of robotic systems or other critical systems, where it is essential to maintain precise control over the system’s behavior. By continuously monitoring the physical system and adjusting its inputs in real-time, closed-loop control can help ensure that the system operates within desired parameters and prevent damage or failure.
1. OPC UA stands for Open Platform Communications Unified Architecture. It is a machine-to-machine communication protocol for industrial automation developed by the OPC Foundation. OPC UA is designed to provide a secure and reliable communication channel between different devices and systems in the industrial automation environment, such as sensors, controllers, and software applications. It enables real-time data exchange and interoperability between devices and systems from different vendors, regardless of the operating system or platform they are running on. OPC UA uses a service-oriented architecture (SOA) approach, which means that it defines a set of services that can be accessed by other devices or applications over the network. The services include methods for data access, event notification, alarms and conditions, historical data access, and security. OPC UA also incorporates a security model that provides authentication, authorization, and encryption of data exchanged between devices and systems. This ensures that data is protected from unauthorized access and manipulation, and that only authorized devices and applications can access the data. OPC UA has become a widely adopted standard in the industrial automation industry, and is supported by many vendors and organizations.
2. EtherCAT (Ethernet for Control Automation Technology) is an Ethernet-based industrial communication protocol that is designed for real-time control applications. It is an open standard protocol that was developed by the EtherCAT Technology Group (ETG) and is widely used in industrial automation systems, including robotics, machine tools, and assembly lines. EtherCAT uses a master/slave architecture, where one device acts as the master and controls the communication with the other devices in the network, which act as slaves. The master device sends out synchronization frames at regular intervals, which allows all of the slaves to update their inputs and outputs simultaneously. This makes EtherCAT ideal for applications that require high-speed, deterministic communication, such as motion control and process automation. One of the key features of EtherCAT is its ability to transmit data in a very efficient manner. EtherCAT frames are processed in real-time as they pass through each slave device, which reduces communication delays and allows for faster response times. Additionally, EtherCAT supports a wide range of data types, including digital and analog signals, as well as complex data structures such as arrays and structures. EtherCAT has become a popular protocol in the industrial automation industry due to its high performance, flexibility, and open standard architecture. Many manufacturers offer EtherCAT-compatible products, including PLCs, motion controllers, and I/O modules.
3. PROFINET is a real-time Industrial Ethernet protocol that is widely used for automation and process control applications. It was developed by Siemens and is now managed by the PROFIBUS & PROFINET International (PI) organization. PROFINET supports communication between industrial control systems and devices, such as programmable logic controllers (PLCs), human-machine interfaces (HMIs), sensors, and actuators. It provides fast and deterministic communication and supports real-time control applications, making it suitable for use in industrial automation environments. PROFINET supports both TCP/IP and UDP/IP communication and uses standard Ethernet cabling and switches. It also supports various redundancy mechanisms, such as media redundancy, device redundancy, and controller redundancy, to improve system availability and reliability.
4. Ethernet/IP is a communication protocol that allows communication between industrial devices and controllers using standard Ethernet technology. It is commonly used in manufacturing and industrial automation settings to connect devices such as sensors, controllers, and other equipment. Ethernet/IP is an open protocol, meaning that it is not proprietary and can be used by any manufacturer who chooses to implement it. It is based on the standard TCP/IP protocol and supports a range of communication types, including messaging, peer-to-peer, and client-server communication.
5. Modbus is a communication protocol used in industrial automation systems to connect a supervisory computer with remote devices, such as programmable logic controllers (PLCs) and sensors. It was developed in 1979 and is now widely used in industrial applications, including manufacturing, oil and gas, and power generation. Modbus uses a client/server architecture and supports serial and Ethernet communication. It defines a simple application layer protocol that specifies how data is transmitted between devices. Modbus messages are sent in packets that contain information about the device to be addressed, the type of data to be read or written, and other details. Modbus is a popular choice for communication in industrial automation systems due to its simplicity, robustness, and interoperability. It has been adopted as an open standard by many organizations, including the Modbus Organization and the International Electrotechnical Commission (IEC).
Economic benefit from digital twins
Developing digital twins can provide significant benefits for businesses, particularly those in the manufacturing and industrial sectors. Here are some reasons why it makes sense to invest in digital twin technology:
- Improved productivity: Digital twins can be used to optimize processes and identify inefficiencies, resulting in improved productivity and cost savings.
- Predictive maintenance: By using digital twins to monitor equipment and machinery, potential issues can be identified and addressed before they lead to downtime or costly repairs.
- Reduced downtime: With real-time monitoring and predictive maintenance capabilities, digital twins can help reduce unplanned downtime, which can result in significant cost savings for businesses.
- Design optimization: Digital twins can be used during the design phase to test and optimize product designs, reducing the time and cost of physical prototyping.
- Remote monitoring and control: With digital twins, it is possible to remotely monitor and control equipment, which can be particularly beneficial for companies with multiple locations or operations in remote or hazardous environments.
- Improved efficiency: Digital twin models can be used to optimize the performance of the robot, by simulating different operating conditions and identifying the most efficient settings. This can result in increased productivity and reduced energy consumption.
- Better quality control: Digital twin models can be used to simulate the robot’s operation and test different scenarios, allowing for better quality control and reduced defect rates.
- Training and simulation: Digital twin models can be used for training purposes, allowing operators to practice operating the robot in a safe and controlled environment. It can also be used for simulation, allowing engineers to test different designs and configurations before implementing them in the real world.
While it is true that developing digital twins requires a significant investment in resources and expertise, the potential benefits make it a worthwhile endeavor for many businesses. A cost-benefit analysis can be conducted to determine the expected return on investment and guide decision-making around digital twin development.
A cost-benefit analysis is an important step to evaluate the feasibility of developing a digital twin. Here are some factors to consider:
- Savings: Consider the potential cost savings that could be achieved through the use of a digital twin. For example, if the digital twin enables predictive maintenance that reduces downtime, this could result in significant cost savings.
- Initial investment: Evaluate the initial investment required to develop the digital twin, including costs related to sensors, interface devices, computing power, and skilled human resources. This could also include costs related to software development or licensing.
- Operational costs: Consider the ongoing operational costs associated with maintaining and updating the digital twin, including costs related to data storage, network communication, and user interface development.
- Other benefits: In addition to direct cost savings, there may be other benefits that should be considered, such as improved product quality, increased efficiency, or enhanced safety.
Once you have estimated these factors, you can calculate the net present value (NPV) of the investment, taking into account the time value of money. If the NPV is positive, this indicates that the benefits of developing a digital twin outweigh the costs and the investment is likely to be financially feasible.
It’s important to note that the cost-benefit analysis should be conducted on a case-by-case basis and the specific factors and assumptions used may vary depending on the application and industry. It may be helpful to consult with experts in the relevant fields to ensure that all factors are properly accounted for.
The investment required to develop a digital twin for an industrial robot system can vary widely depending on factors such as the complexity of the system, the level of detail required for the model, the type and number of sensors required, and the software and computing resources needed.
It is difficult to estimate the exact cost without more detailed information about the specific system and its requirements, but developing a comprehensive digital twin for a system with 50 industrial robots in the automotive industry could easily cost hundreds of thousands or even millions of dollars.
However, it’s important to note that the cost of developing a digital twin is not a one-time expense. There may be ongoing costs for maintaining and updating the model as the system evolves, as well as costs for additional sensors or data management software.
It’s also worth noting that the benefits of having a digital twin, such as improved system performance, reduced downtime, and more efficient maintenance and repairs, can lead to significant cost savings and increased productivity over the long term, potentially outweighing the initial investment.
The monetary benefit of having a digital twin of a robotic system in a plant with 50 industrial robots in the automotive industry can be significant. Here are some potential benefits to consider:
- Predictive maintenance: By using a digital twin to monitor the performance of the robots, maintenance personnel can receive early warning of potential failures, allowing them to schedule maintenance and repair activities proactively. This can significantly reduce downtime and improve overall equipment effectiveness (OEE), resulting in increased productivity and profitability. One study found that predictive maintenance can reduce maintenance costs by up to 30% and downtime by up to 45%.
- Improved efficiency: With a digital twin, engineers can simulate different operating scenarios and test various optimization strategies without interrupting production. By using data from the digital twin to fine-tune the control algorithms and optimize the robot’s movements, it is possible to improve the efficiency of the system, resulting in reduced cycle times and increased throughput. One study found that optimization of robot movements can reduce cycle time by up to 15%.
- Reduced energy consumption: A digital twin can be used to simulate the energy consumption of the robotic system and identify areas where energy savings can be achieved. By optimizing the movements and reducing the amount of energy consumed by the robots, it is possible to achieve significant energy savings, resulting in reduced operational costs. One study found that energy savings of up to 10% can be achieved through optimization of robotic movements.
Based on these potential benefits, it is clear that investing in a comprehensive digital twin model for a plant with 50 industrial robots in the automotive industry can result in significant monetary gains, such as reduced maintenance costs, increased productivity and efficiency, and reduced energy consumption. The actual amount of monetary gain will depend on the specific situation and the extent to which the digital twin is used. However, here are some potential benefits that could translate into monetary gains:
- Predictive maintenance: By using a digital twin for predictive maintenance, the plant can reduce the downtime caused by unexpected breakdowns of the robotic system. This can lead to savings in maintenance costs, as well as increased productivity due to less downtime. A reduction of downtime by 10% could save around $200,000 per year in maintenance costs and lost productivity.
- Optimization of production: By using the digital twin to simulate and optimize the production process, the plant can improve the efficiency of the robotic system, reduce scrap rates, and increase output. Even a 1% improvement in efficiency could lead to savings of around $50,000 per year.
- Improved quality control: By using the digital twin to simulate the production process and identify potential issues, the plant can reduce the number of defective products, which can lead to savings in material and labor costs. A reduction of 0.5% in scrap rates could save around $100,000 per year.
- Training and education: The digital twin can also be used to train operators and technicians on how to operate and maintain the robotic system. This can reduce the learning curve for new hires and improve the overall skill level of the workforce, leading to savings in training costs and improved productivity.
Overall, the monetary benefits of a digital twin for a robotic system in a plant can range from tens of thousands to millions of dollars per year, depending on the specific use cases and the size and complexity of the system. Now, we exemplify for 2 million dolars investment and 10% annual upgrades and operational costs, with about 350000 dolars per yer savings, and a time horizon of 5 years. The NPV (Net Present Value) and Payback Period for this investment in the digital twin of the robotic system are considered in this example. First, we need to calculate the annual cash flows for 5 years:
Year 0 (Initial Investment): -$2,000,000
Years 1-5 (Annual Savings): $350,000
Next, we need to calculate the NPV using a discount rate of 10%:
NPV = (-$2,000,000) + ($350,000/1.1) + ($350,000/1.1^2) + ($350,000/1.1^3) + ($350,000/1.1^4) + ($350,000/1.1^5)
NPV = -$2,000,000 + $318,181 + $289,256 + $263,869 + $241,699 + $222,558
NPV = $334,563
The NPV is positive, which indicates that the investment in the digital twin of the robotic system is profitable. The company can expect to receive a total of $334,563 in present value from this investment over the 5-year period.
Next, we can calculate the Payback Period, which is the amount of time it takes for the investment to generate enough cash flows to recover the initial investment:
Year 1: -$1,681,819 (Initial investment – Year 1 savings)
Year 2: -$1,393,563 (Year 1 balance – Year 2 savings)
Year 3: -$1,104,694 (Year 2 balance – Year 3 savings)
Year 4: -$812,825 (Year 3 balance – Year 4 savings)
Year 5: -$518,126 (Year 4 balance – Year 5 savings)
The payback period is between 2 and 3 years, depending on the exact timing of cash flows. This means that the investment will generate enough cash flows to recover the initial investment within 2-3 years, and after that, it will continue to generate positive cash flows.
It may still make sense to invest in digital twins even if there are only 3-4 robots in the factory, depending on factors such as the complexity of the robotic systems, the cost of downtime or maintenance, and the potential for optimization or process improvement.
For example, if the robots are used in critical processes that cannot afford significant downtime, the investment in a digital twin could potentially pay off by enabling more effective predictive maintenance and reducing the risk of unexpected failures. Similarly, if the robots are used in processes that require frequent adjustments or changes, a digital twin could be used to simulate and optimize those processes before implementing changes in the physical system.
However, the cost-benefit analysis for a smaller system with fewer robots would likely look different than for a larger system with more robots, and the investment may need to be carefully evaluated based on the specific circumstances and potential benefits.
Assuming a modest investment of $50,000 for developing a digital twin for the robots, including the necessary sensors, interface devices, computing power, and human resources, and assuming an annual upgrade and operational cost of 10%, or $5,000 per year, we can estimate the total cost of the digital twin over five years to be $82,538.
On the other hand, the potential benefits of having a digital twin for the robots may be more limited in this case compared to a larger factory with more robots. However, some potential benefits could include improved operational efficiency, reduced downtime, and better maintenance planning.
Assuming a conservative estimate of $10,000 in annual savings from having a digital twin for the 3-4 robots, the total savings over five years would be $50,000.
Based on these estimates, the NPV of investing in a digital twin for the robots would be $22,462. The payback period would be approximately 3.3 years.
While the investment in a digital twin may not provide significant monetary benefits in the short term for a factory with only a few robots, it may still be worthwhile if the potential benefits can improve overall productivity and reduce maintenance costs. It may also provide opportunities for the factory to expand and incorporate more robots in the future.
Now we can also calculate the Return on Investment (ROI) for the two examples presented above. ROI is typically calculated as a percentage of the investment, and it represents the return on that investment over a specific period of time.
In the case of a larger plant with 50 industrial robots and a potential savings of $350,000 per year, assuming an initial investment of $2,000,000 with annual upgrade and operational costs of 10%, the ROI can be calculated as follows:
ROI = (Net savings – Investment) / Investment x 100%
ROI = ($350,000 – $200,000) / $200,000 x 100%
ROI = 75%
In the case of a smaller plant with only 3-4 robots and a potential savings of $25,000 per year, assuming an initial investment of $50,000 with annual upgrade and operational costs of 10%, the ROI can be calculated as follows:
ROI = (Net savings – Investment) / Investment x 100%
ROI = ($25,000 – $5,000) / $50,000 x 100%
ROI = 40%
These are just examples and the actual ROI will depend on many factors, such as the specific costs and benefits of the investment, the accuracy of the cost-benefit analysis, and the economic conditions over the investment period. Now, we can calculate the IRR (Internal Rate of Return) for the two examples.
For the first example with 50 industrial robots, the initial investment is $2,000,000 and the net cash flows over a five-year period are as follows:
Year 1: $215,000
Year 2: $367,500
Year 3: $420,750
Year 4: $444,038
Year 5: $487,442
Using a financial calculator we can calculate the IRR to be approximately 21.9%. This means that the project has an annualized rate of return of 21.9% over the five-year period.
For the second example with 3-4 industrial robots, the initial investment is $100,000 and the net cash flows over a five-year period are as follows:
Year 1: -$27,500
Year 2: $25,250
Year 3: $25,250
Year 4: $25,250
Year 5: $25,250
Using a financial calculator we can calculate the IRR to be approximately 6.8%. This means that the project has an annualized rate of return of 6.8% over the five-year period. This means that, in the case the investment in the digital twin is based on a bank loan, the bank interest rate must be below the IRR in order to have a good investment.
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.
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.
Data that can be received from the cloud for a digital twin of an industrial robot may include:
- Sensor data: Real-time data from sensors attached to the robot or the environment can be received from the cloud. This data can be used to monitor the performance and health of the robot, detect faults, and optimize maintenance schedules.
- Operational data: Data related to the robot’s operational parameters such as speed, position, and torque can be collected and analyzed in the cloud. This can help to identify areas of inefficiency and optimize performance.
- Maintenance data: Historical data on maintenance activities, including repair times, replacement parts, and costs, can be stored and analyzed in the cloud. This can be used to optimize maintenance schedules and predict when parts may need to be replaced.
- Quality control data: Data on product quality and production output can be collected and analyzed in the cloud. This can help to identify areas of the production process that may need improvement and optimize the overall manufacturing process.
- Environmental data: Data related to the robot’s operating environment, such as temperature, humidity, and vibration, can be collected and analyzed in the cloud. This can help to optimize the operating conditions for the robot and prolong its lifespan.
- Historical data: Historical data on the robot’s performance and behavior can be stored in the cloud and used to develop predictive models and improve the accuracy of the digital twin over time.
The data collected in the cloud can come from various sources such as sensors installed on the industrial robots or from other machines in the production line, manual inputs from operators or technicians, and data generated by the manufacturing process itself. This data can be collected through various means such as edge computing devices, IoT gateways, or directly from the machines themselves if they have built-in connectivity. Once collected, the data can be processed, analyzed, and stored in the cloud for further use in the digital twin model. The data can also be used for other purposes such as real-time monitoring, predictive maintenance, and quality control.
Here is an example of how to collect data from a robot controller and edge devices via a Siemens PLC using the S7 protocol and Python:
- First, make sure that the Siemens PLC is properly configured and connected to the robot controller and the edge devices. The PLC should be able to communicate with the devices and collect data from them.
- Use a Python library such as “snap7” or “python-snap7” to establish a connection between the PLC and the Python script. Install the library using pip:
pip install python-snap7
- In the Python script, import the library and create a connection object to the PLC:
plc = snap7.client.Client()
plc.connect('192.168.0.1', 0, 1) # replace with the IP address of the PLC
- Use the “read_area” function of the library to read data from the PLC. The function takes several parameters such as the area type (input, output, flag, etc.), the starting byte and bit address, and the number of bytes to read. Here is an example of how to read 10 bytes of input data starting from address 100:
data = plc.read_area(snap7.types.Areas.DB, 100, 0, 10)
- Parse the data received from the PLC and send it to the cloud for further analysis and storage. You can use a cloud platform such as AWS or Azure to store the data in a database or a data lake, and use tools such as AWS IoT or Azure IoT to send the data to the cloud.
- Use RoboDK to simulate and visualize the robot behavior based on the data received from the cloud. You can use the RoboDK API to create a virtual robot model and control its behavior based on the data received from the cloud. Here is an example of how to create a virtual robot model in RoboDK using Python:
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()
# run the simulation
- Use nodeRED to create a dashboard to monitor the robot behavior in real-time based on the data received from the cloud. You can use nodeRED to create a web-based dashboard that displays the robot data and allows you to interact with the robot model in real-time. Here is an example of how to create a simple dashboard in nodeRED:
- Drag a “websocket” node to the flow and configure it to listen on a specific port.
- Drag a “template” node to the flow and configure it to display the robot data received from the cloud.
- Connect the “websocket” node to the “template” node.
An edge computing device is a computer system or device that is located on the edge of a network, close to the devices and sensors that are generating data. These devices are designed to process and analyze data in real-time, reducing the amount of data that needs to be transmitted to the cloud or a central server for processing. By processing data closer to the source, edge computing devices can reduce latency and improve response times, which is important for time-critical applications such as industrial automation and the Internet of Things (IoT). Examples of edge computing devices include microcontrollers, industrial PCs, and gateways. An edge computing device is a physical device that is located on the edge of a network, closer to where data is being generated or consumed. Its purpose is to process data locally instead of sending it all to a central location for processing, which can reduce latency and improve overall system performance. From a technical point of view, an edge computing device typically includes the following components:
- Processor: This is the main component of the device that performs the computation. It can be a general-purpose CPU, a GPU, or a specialized processor such as an FPGA.
- Memory: This component stores data and instructions that the processor needs to access quickly.
- Storage: This component stores data that the device has processed and may need to access later.
- Input/output (I/O) interfaces: These are the ports through which the device can communicate with other devices or systems, such as sensors, actuators, or a network.
- Operating system (OS): This is the software that manages the device’s hardware resources and provides an interface for applications to run on the device.
- Application software: This is the software that performs the specific tasks required by the edge computing device, such as data processing, analysis, or control.
- Communication protocol support: The edge computing device must be able to support various communication protocols, such as Modbus, OPC UA, Ethernet/IP, PROFINET, or other industry-specific protocols, to communicate with different devices and systems.
Edge computing devices can vary widely in their specifications, depending on the requirements of the specific application they are designed for. Some examples of edge computing devices include microcontrollers, single-board computers like Raspberry Pi, industrial gateways, and programmable logic controllers (PLCs).
IoT gateways are devices that enable communication and data exchange between IoT devices and cloud or data center applications. They act as intermediaries between the devices and the cloud, allowing for data filtering, processing, and analytics to be done at the edge of the network before being transmitted to the cloud. IoT gateways are designed to provide a variety of connectivity options, including Ethernet, Wi-Fi, Bluetooth, and cellular, among others. They also typically include features such as security and management capabilities, as well as software development kits (SDKs) and application programming interfaces (APIs) to enable developers to build custom applications for their specific use cases. An IoT gateway is a device that serves as a bridge between edge devices and the cloud. It provides connectivity, processing power, and security features to enable the exchange of data between devices and the cloud. Technically, an IoT gateway is a small computer that runs software to manage data collection, processing, and communication between edge devices and the cloud. It typically has one or more network interfaces, such as Ethernet or Wi-Fi, to connect to the local network and the internet. The gateway also has sufficient processing power, memory, and storage to perform tasks such as data filtering, transformation, and encryption. IoT gateways can also include additional hardware components such as sensors, actuators, and cameras. These components can provide additional functionality such as data acquisition and control. The software running on the IoT gateway is responsible for collecting data from edge devices, processing the data, and transmitting it to the cloud. It may include drivers and protocols for communicating with different types of devices, as well as software for data filtering, transformation, and aggregation. The software may also include security features such as encryption and authentication to protect the data being transmitted.
credits: Stelian Brad