In modern industrial automation, the integration of intelligent robots into assembly workstations has become increasingly complex, leading to challenges in visualization, real-time interaction, and data synchronization. Traditional monitoring systems often lack the capability to provide comprehensive, dynamic insights into the operational status of these systems. To address these issues, we propose a remote monitoring system for an intelligent robot assembly workstation leveraging digital twin technology. This system enables full-process visualization, real-time data exchange, and synchronized operation between physical and virtual environments, enhancing the safety and efficiency of intelligent robot applications.
The core of our approach lies in constructing a multidimensional digital twin model that encapsulates geometric, physical, behavioral, and rule-based characteristics of the intelligent robot workstation. By digitizing the behavior and processes of physical objects, we create a high-fidelity virtual representation that mirrors the real-world system. This digital twin facilitates remote monitoring through a customized platform developed using NX MCD, coupled with a data acquisition system based on the industrial robot PC SDK. Communication between the physical and virtual systems is achieved via OPC UA, ensuring reliable and real-time data transmission. Our system not only allows for dynamic model updates based on live data but also supports remote control and debugging, ultimately reducing development cycles and costs while improving operational reliability.
The overall architecture of the digital twin-based remote monitoring system is structured into three main layers: the upper computer monitoring layer, the network connection layer, and the physical control layer. This hierarchical design ensures seamless integration and data flow across the system. The upper computer layer comprises the remote monitoring system and the digital twin system, which are responsible for data visualization, model interaction, and control commands. The network layer employs various communication protocols, such as TCP/IP and OPC UA, to bridge the gap between virtual and physical components. Lastly, the physical control layer includes the actual intelligent robot, PLC controllers, sensors, and other peripheral devices that execute the assembly tasks. This architecture supports bidirectional data exchange, enabling the digital twin to reflect real-time changes in the physical intelligent robot workstation and vice versa.
To formalize the digital twin model, we define it as a combination of multiple dimensions, represented by the following equation:
$$ \sigma_{DtM} = \{\sigma_{GeM}, \sigma_{PhyM}, \sigma_{BehM}, \sigma_{RulM}\} $$
Here, $\sigma_{DtM}$ denotes the digital twin model, which integrates geometric ($\sigma_{GeM}$), physical ($\sigma_{PhyM}$), behavioral ($\sigma_{BehM}$), and rule-based ($\sigma_{RulM}$) features. Each dimension contributes to a comprehensive virtual representation of the intelligent robot assembly workstation. For instance, the geometric model captures static attributes like size and shape, while the physical model incorporates dynamic properties such as mass and friction. The behavioral model defines motion and interaction patterns, and the rule model ensures consistency with real-world operational constraints.
In the geometric model construction, we focus on creating accurate 3D representations of the intelligent robot and its associated components, such as rotary feeding mechanisms, positioners, and conveyor systems. Using NX MCD, we develop detailed models that include dimensions, shapes, and topological structures. The physical model extends this by assigning material properties, mass, and inertia to simulate real-world behavior. For example, the intelligent robot’s joints are modeled with specific mass and friction parameters to replicate actual movement dynamics. Table 1 summarizes key geometric and physical attributes for major components of the intelligent robot workstation.
| Component | Geometric Attributes | Physical Attributes |
|---|---|---|
| Intelligent Robot | Size: 500x300x200 mm, Shape: Articulated | Mass: 25 kg, Friction: 0.1 |
| Rotary Feeder | Size: 150x150x100 mm, Shape: Cylindrical | Mass: 5 kg, Inertia: 0.05 kg·m² |
| Positioner | Size: 200x200x150 mm, Shape: Rectangular | Mass: 10 kg, Move: Linear |
| Conveyor | Size: 1000x200x50 mm, Shape: Belt | Mass: 15 kg, Friction: 0.2 |
The behavioral model is crucial for simulating the dynamic operations of the intelligent robot workstation. It involves defining constraints, assembly sequences, and motion behaviors through kinematic pairs and sensors in NX MCD. For instance, we implement hinge joints for the intelligent robot’s arms, sliding pairs for linear actuators, and collision sensors to detect interactions between components. This model enables the virtual intelligent robot to perform tasks such as gripping, moving, and assembling workpieces in sync with the physical system. The behavioral model can be expressed mathematically to describe motion trajectories, as shown in the equation for joint position control:
$$ \theta(t) = \theta_0 + \int_0^t \omega(\tau) d\tau $$
where $\theta(t)$ is the joint angle at time $t$, $\theta_0$ is the initial angle, and $\omega(\tau)$ is the angular velocity. This ensures that the virtual intelligent robot mimics the real one accurately.
Rule models govern the operational logic and data exchange between the physical and virtual systems. They are implemented using signal adapters in NX MCD, which map external data from controllers to the digital twin parameters. For example, the positioner model uses drive rules to convert OPC UA signals into motion commands, maintaining synchronization. The rule model for the intelligent robot can be represented as a set of logical conditions:
$$ \text{IF } S_{\text{sensor}} = \text{TRUE THEN } A_{\text{robot}} = \text{EXECUTE} $$
where $S_{\text{sensor}}$ is a sensor signal and $A_{\text{robot}}$ is an action of the intelligent robot. This ensures that the digital twin responds to real-time inputs from the physical workstation.
For the remote monitoring system, we developed a data acquisition module using the industrial robot PC SDK, which provides API access to controller data such as joint positions, I/O states, and program status. This module collects real-time data from the intelligent robot and transmits it via TCP/IP to the upper computer. Additionally, we built an OPC UA server to facilitate communication between the data acquisition system and the digital twin model. The server exposes nodes for critical parameters, allowing the virtual model to update based on live data. Table 2 outlines the data types and communication methods used in the monitoring system.
| Data Type | Source | Communication Protocol | Update Frequency |
|---|---|---|---|
| Joint Positions | Intelligent Robot | TCP/IP | 100 Hz |
| I/O Signals | PLC | Modbus TCP | 50 Hz |
| Sensor Data | Positioner | OPC UA | 200 Hz |
| Program Status | Robot Controller | PC SDK | 10 Hz |
The OPC UA server implementation involves creating a structured address space with nodes representing the intelligent robot’s variables, such as joint angles and tool positions. The server processes read and write requests, updating the digital twin model in real time. The data flow can be described by the following equation for signal mapping:
$$ V_{\text{virtual}} = f(S_{\text{external}}) $$
where $V_{\text{virtual}}$ is a parameter in the digital twin and $S_{\text{external}}$ is an external signal from the OPC UA server. This function ensures that changes in the physical intelligent robot are immediately reflected in the virtual environment.
During system deployment and debugging, we focused on ensuring the digital twin model operates consistently with the physical intelligent robot workstation. For instance, we adjusted acceleration parameters in the virtual model to match real-world motion profiles, as excessive values caused jerky movements. The position, velocity, and acceleration curves for the intelligent robot’s first joint were optimized using the following kinematic equation:
$$ a(t) = \frac{d\omega(t)}{dt} $$
where $a(t)$ is acceleration and $\omega(t)$ is angular velocity. By setting the acceleration to 500°/s², we achieved smooth operation, whereas the default 6000°/s² led to instability. This tuning process involved iterative testing to eliminate issues like workpiece drops or collisions in the virtual environment.
The data acquisition system was debugged by verifying connectivity and data accuracy through the PC SDK interfaces. We monitored joint data and program execution states, ensuring that the remote control functions, such as start/stop and program download, worked reliably. The intelligent robot’s status is continuously tracked and displayed on the upper computer, enabling operators to intervene if anomalies occur. Table 3 provides a summary of key performance metrics during debugging.
| Metric | Virtual System | Physical System | Tolerance |
|---|---|---|---|
| Assembly Time | 3.5 min | 3.5 min | ±0.1 min |
| Position Error | < 0.5 mm | < 0.5 mm | ±0.1 mm |
| Data Latency | 10 ms | 10 ms | ±5 ms |
| Communication Rate | 95% | 95% | ±2% |
In the final system integration, we connected the remote monitoring platform, digital twin system, intelligent robot controller, and PLC via an industrial Ethernet network. The virtual and physical systems operated synchronously, completing the assembly of a workpiece set in 3.5 minutes with no deviations. This demonstrates the effectiveness of our digital twin approach in achieving real-time synchronization and visualization for intelligent robot workstations. The system’s ability to dynamically update models based on live data ensures that operators can monitor and control the assembly process remotely, enhancing productivity and safety.
In conclusion, our digital twin-based remote monitoring system for intelligent robot assembly workstations addresses key challenges in industrial automation by providing a holistic, real-time solution. Through multidimensional modeling and robust data communication, we enable seamless interaction between virtual and physical environments. Future work could expand this framework to include predictive maintenance and AI-driven optimization, further advancing the capabilities of intelligent robots in smart manufacturing.