LS ELECTRIC XG-SIM Virtual Commissioning for Cartesian Coordinate Robots

Brand: LS ELECTRIC

Date: August 2025

Introduction

This research verifies the Virtual Commissioning technology based on XG-SIM (Virtual PLC) for Cartesian coordinate robots, utilizing Digital Twin. The study aims to execute virtual commissioning for Cartesian coordinate robots using LS ELECTRIC's XG-SIM and evaluate the consistency between real and virtual data to assess its practicality.

Background and Technology

Digital Twin Technology

Digital Twin refers to a virtual representation of a physical object, system, or process. In the context of Digital Twin, Virtual Commissioning technology enables the use of real-time data, simulation, and various modeling techniques. LS Virtual PLC (XG-SIM) facilitates Virtual Commissioning technology to achieve the same level of operation as actual equipment in a virtual environment.

Isochronous Mode

Isochronous Mode synchronizes data communication between virtual and real environments, ensuring accuracy and simultaneity. This mode helps prevent data loss during the Virtual Commissioning process.

System Configuration

The system configuration involves using NX MCD's digital twin model and EtherCAT-based Servo Drives, virtualized as a Servo Emulator (FMU). This setup allows for data collection and comparative analysis under conditions identical to those of a real robot.

Control of Servo Motor's precise position, speed, and torque.
Servo Motor operation based on commands transmitted from the PLC.
Ensuring accurate motion and control functions through the feedback system.

Real Process Flow: The real process involves 3D model design, mechanical part fabrication, electrical component manufacturing, assembly, wiring, PLC programming, signal and mechanical operation verification, and commissioning.

Digital Twin Process Flow: The Digital Twin process involves 3D model design, 3D model condition setup, data generation, virtual simulation, PLC program development (using XG-SIM instead of XG5000), data mapping, and virtual commissioning. This includes setting up MCD 3D conditions, generating MCD data (Mass, Inertia, Friction, Joint Sensor, Command, Feedback), mapping slave information (Sensor, Command, Feedback), and performing virtual commissioning using specialized Digital Twin software like NX MCD or Visual Components.

Diagram Description (Page 3): The diagram illustrates the flow from real process components (Mechanical elements, Electrical components like PLC and Servo Drives, PLC program XG5000) to Digital Twin process components (Digital Twin model creation, Virtual PLC and Servo Emulator configuration, PLC program XG-SIM, Virtual commissioning). It highlights the use of FMI for Co-Simulation and tools like NX MCD and Visual Components.

Research Results and Analysis

Experiment 1: Isochronous Mode Verification (NX MCD & XG-SIM Loopback Test)

The test confirmed no data loss during data transmission between NX MCD and XG-SIM in Isochronous Mode, indicating data consistency.

Data Table Description (Page 4): A table shows 'Samples', 'XG-SIM Speed Command', 'MCD Loopback Result', and 'Data loss'. All 'Data loss' values are 0, confirming consistency.

Graph Description (Page 4): A graph titled "Isochronous Mode Speed Command Data Loss 'Zero'" plots 'Error' against 'Samples'. The error is consistently zero, visually confirming the data consistency.

Graph Description (Page 4): Another graph titled "Loopback Test Results (Data Error)" plots 'Feedback_Position [mm]' against 'Samples'. The graph shows a consistent output between the simulated and actual values, with a note indicating '0' (Data Consistency).

Experiment 2: XG-SIM Reliability Verification (Real Robot & Digital Twin Model Alignment Analysis)

This experiment visually confirmed the real robot's operation against the virtual model's predicted operation in real-time. Error analysis of feedback data per axis was performed to verify the Digital Twin's reliability.

Image Description (Page 5): Images show the Cartesian coordinate robot setup and the Digital Twin simulation interface on monitors.

Graph Description (Page 5): A graph titled "X-Y Axis Position Coordinates" compares the 'Actual Position' of the real robot and the MCD model. It shows the 'Real Robot vs. Virtual Digital Twin' and the 'Error between Real Robot and NX MCD Position' (ACT_POS - MCD ACT_POS). The maximum error is reported as 0.63 mm, with an average error of 2.45e-05 mm.

Graph Description (Page 6): This page presents detailed error analysis. It compares the performance of the real robot and the NX MCD's physics engine using Virtual Commissioning technology. It shows graphs of 'DT Error (AXIS1_CMD - AXIS1_ACT)' for the DT model (Max Error = 1.15mm) and 'Real Robot Error (CMD-ACT)' for the actual robot (Max Error = 1.75mm). Another graph compares the 'DT Model Error & Real Robot Error Overlay', showing a high correlation (R² = 88.91%). A final graph shows 'Real Robot vs. DT Model Position Error' (ACT_POS - DT Model ACT_POS), with a Max Error of 0.63 mm and Avg. Error of 2.45e-05 mm.

Conclusion

This study confirmed high data consistency and precision in virtual commissioning of Cartesian coordinate robots using LS ELECTRIC's XG-SIM. The Virtual Commissioning technology utilizing XG-SIM offers potential applications in:

Verifying mechanical interference through virtual commissioning before equipment manufacturing.
Optimizing control simulations and programs.
Providing guidance for motor capacity selection (motor selection within Servo Emulator).

Diagram Description (Page 7): A diagram illustrates the integration of XGT Virtual PLC with various applications like Automated Warehouse, Conveyance Systems, and Semiconductor/Battery Manufacturing Equipment, showcasing the broad applicability of the technology.

References

C.G. Lee et al., 2014, "Survey on the virtual commissioning of manufacturing systems J. Comput. Des. Eng"
Jingxi Zhang et al., 2025, "Digital twin and the asset administration shell"
Jinjiang Wang et al., 2023, "Digital twin-driven virtual commissioning of machine tool"
M. Sc. Suthida Thongnuch, 2021, "An approach to generating high-fidelity models for the virtual commissioning of specialized production machines and cells using MCAD models"

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