Provides an intuitive 3D virtual production line building environment with a built-in library of standardized equipment models, including industrial robots, CNC machining centers, conveyors, AGVs, sensors, and assembly workstations. Users can quickly build customized production line layouts through drag-and-drop operation, simulate the spatial layout and logistics flow of real production environments, and support more efficient production line planning and layout verification .
Uses a digital twin architecture to support data connectivity between the virtual production line and real equipment. The system can receive operating data from a real production line in real time and synchronously display equipment status, production progress, and process parameters in the virtual space. This supports visualized monitoring and provides teaching support for remote operation and maintenance as well as predictive maintenance.
Covers typical smart manufacturing process scenarios, including machining (turning, milling, drilling), component assembly (bolt fastening and component fitting), quality inspection (dimensional measurement and visual recognition), packaging, palletizing, and related full-process simulation. Parameter variables can be configured at each process node to simulate process adjustment under different product specifications and production batches.
Includes a simplified MES (Manufacturing Execution System) simulation module to simulate core business processes such as production order management, work order scheduling, operation reporting, material pull, and finished goods warehousing. Learners can practice MES operating procedures, understand how production plans are converted into specific work instructions, and build digital production management thinking.
Integrates a SCADA data acquisition engine to extract key production indicators from virtual equipment in real time, such as output, yield rate, equipment utilization, and OEE. The system provides visual dashboards and data analysis panels, presenting production status through line charts, bar charts, pie charts, and other chart types to help learners develop data-driven production management awareness.
Built-in typical equipment fault scenarios include robot end-effector offset, sensor failure, abnormal cylinder motion, conveyor jamming, and related fault cases. Both random triggering and manual setting modes are supported. Learners complete troubleshooting and emergency handling within a specified time. The system records the diagnostic path and handling results automatically and generates a dedicated evaluation report.
Uses a modular design concept. Production line units such as workstations, equipment, and logistics nodes can be configured independently and combined freely. Users can flexibly adjust production line scale, from a single workstation to a complete line, as well as the process sequence and production takt according to teaching needs. The platform supports simulation of multi-variety, small-batch production scenarios and adapts to different industries and professional training needs.
|
Parameter |
Value |
Parameter |
Value |
|
Simulation Platform Type |
B/S architecture web application |
Operating Environment |
Windows 10/11 (64-bit) |
|
3D Rendering Engine |
WebGL / Three.js |
Maximum Concurrent Users |
50 users |
|
Built-in Simulation Model Categories |
8 major categories (robots, CNC, AGVs, conveyors, sensors, etc.) |
Total Number of Models |
120+ standard models |
|
Supported Process Workflow Types |
Machining, assembly, inspection, packaging, logistics |
Number of Process Nodes |
Supports 20+ linked processes |
|
Digital Twin Interface Protocols |
OPC UA, Modbus TCP, MQTT |
Data Acquisition Points |
1000+ points |
|
MES Simulation Module |
Order management, work order scheduling, operation reporting, material pull |
Network Interface |
Ethernet / RJ45 |
|
Built-in Teaching Courses |
15+ sets |
Built-in Fault Scenarios |
30+ types |
|
Scoring Dimensions |
6 major dimensions (operation standards, response speed, parameter setting, etc.) |
Report Export Formats |
Excel / PDF |
|
Teaching Resource Formats |
Video tutorials, PDF courseware, practical training guides |
Authorization Method |
Site authorization / user authorization |
|
Recommended Hardware Configuration |
CPU i7 / 16 GB RAM / GTX 1660 graphics card |
Display Resolution |
1920 x 1080 or above |
Suitable for core course teaching and practical training in smart manufacturing, automation, mechatronics, and related majors in vocational colleges. Teachers can use the system for theoretical instruction and virtual training in courses such as Industrial Robot Operation and Programming, PLC Control Technology Application, and Digital Production Line Planning. Students can practice repeatedly in a safe virtual environment to strengthen professional skills.
Designed for onboarding training and in-service skills upgrading in small and medium-sized manufacturing enterprises. It provides standardized training courses for production line operation and maintenance. Enterprises can customize simulation scenarios based on their own production line features, helping employees become familiar with production workflows, equipment operation standards, and emergency handling procedures for abnormal situations.
Supports engineering practice for enterprise technical planning personnel, vocational college teachers, and students in new production line planning and existing production line optimization. Before actual construction or transformation, the simulation system can be used to verify the feasibility of production line layouts, logistics routes, and production takt, helping identify potential issues and support optimization at an early stage.
Can be used as a simulation training platform and assessment environment for smart manufacturing skills competitions. It supports multiple modes, including individual operation and multi-person collaboration. The system includes a standardized scoring system and assessment integrity mechanisms, which help objectively evaluate professional knowledge, operational skills, and comprehensive analysis capabilities. It is suitable for school-level, municipal-level, and industry-level skills competition scenarios.
Q: What computer configuration is required for the Smart Production Line Simulation System?
A: The system is designed with B/S architecture and has relatively friendly requirements for client computers. The recommended configuration is an Intel Core i7 processor or an AMD processor with equivalent performance, 16 GB RAM, an NVIDIA GTX 1660 or above graphics card, and a 1920 x 1080 resolution display. The minimum configuration is an Intel Core i5 processor, 8 GB RAM, and integrated graphics. Chrome or Edge browser is recommended for a better experience.
Q: Does the system support secondary development or customization?
A: Yes. The system supports a certain level of secondary development and customization, including adding or replacing 3D equipment models, customizing process workflows and parameter configurations, adjusting scoring rules and evaluation dimensions, and connecting with existing MES systems or PLC equipment data. Deep customization development should be evaluated according to specific requirements, workload, and cost. Direct communication with the technical team is recommended.
Q: Can the system connect with real production equipment?
A: Yes. The system supports integration with real equipment. It provides compatible interfaces for mainstream industrial communication protocols such as OPC UA, Modbus TCP, and MQTT, enabling data connections with real PLC equipment, industrial robots, sensors, AGVs, and related devices. This enables virtual-physical linkage between the virtual environment and real production lines. On-site evaluation of equipment models and communication conditions is required, and the connection plan should be confirmed with technical support before procurement.
Q: How does the system support fault diagnosis teaching?
A: The system includes 30+ typical equipment fault scenarios, covering mechanical faults, electrical faults, sensor abnormalities, software logic errors, and other common fault types. Teachers can set fault triggering methods flexibly, either random triggering or manual assignment. Learners troubleshoot based on alarm information, HMI interface data, equipment operating status, and other clues. The system automatically records the troubleshooting process and generates diagnostic reports for teacher review and learner reflection.