The rapid advancement of electric car technology places unprecedented demands on manufacturing, particularly for critical components like wheel hubs. These components require high precision, consistent quality, and the ability to accommodate diverse designs to match various electric car models. Traditional production methods, often reliant on manual operation of CNC lathes, are increasingly inadequate. Their efficiency and quality are constrained by operator skill, leading to bottlenecks and inconsistencies that are incompatible with the scale and quality requirements of modern electric car production. While some manufacturers have attempted automation using basic robotic systems, challenges with stability, precision, and integration have limited their success. Driven by market demands for customization and technological evolution, the manufacturing of electric car wheel hubs is transitioning towards flexible, intelligent production systems. This article details the design and implementation of an integrated intelligent machining line, demonstrating that the deep integration of industrial robots, vision systems, quality inspection, and production management software can create a synergistic, highly efficient, and sustainable manufacturing model for the electric car industry.
Foundational Concepts and System Overview
The design of an intelligent production line is a systematic endeavor that requires a holistic approach. The core principles guiding our design for the electric car wheel hub line are parametric planning, process-centric layout, and modular integration.
Application Fundamentals of Industrial Robots
In designing robotic workstations and lines, a parametric methodology provides the essential framework for system planning. The equipment layout must center on the machining process, synthesizing critical factors such as:
| Design Factor | Considerations |
|---|---|
| Part Processing Characteristics | Material, geometry, tolerances, and required surface finish for the electric car wheel hub. |
| Equipment Function Matching | Compatibility between robot payload/reach, CNC machine specifications, and auxiliary equipment. |
| Material Handling & Flow | Design of conveyors, pallet systems, and robot trajectories to minimize idle time. |
| Workpiece Positioning & Fixturing | Design of fixtures and secondary positioning units to ensure machining accuracy. |
| Safety & Human-Machine Collaboration | Implementation of fences, light curtains, and safe-speed zones for operator interaction. |
By systematically analyzing these elements, a production line layout that is both technically sound and economically viable can be formulated. This approach not only enhances manufacturing orderliness and efficiency but also establishes a robust foundation for future technological upgrades.
Principles of Vision Inspection Technology
Vision inspection is pivotal for achieving the stringent quality standards required for electric car components. The technology is based on optical imaging and digital image processing. An industrial camera, equipped with specialized lenses and controlled lighting, captures images of workpieces. Sophisticated algorithms then process these images to perform critical functions such as dimensional measurement, defect detection, and precise localization. The system architecture comprises three main modules:
- Mechanical Structure: Ensures stable part presentation and transport.
- Electrical & Lighting Unit: Synchronizes cameras, lighting, and sensors.
- Software System: Executes image preprocessing, feature extraction, and decision-making.
The integration of hardware and software enables high-speed, high-precision, and stable automated inspection, effectively replacing error-prone manual checks. Furthermore, the vision system’s deep integration with robots and PLCs allows for real-time data sharing and closed-loop process control, forming a cornerstone for the digital and intelligent transformation of electric car parts manufacturing.
Virtual Commissioning and Operational Monitoring
Virtual commissioning is a key enabler for intelligent machining lines. By creating a high-fidelity digital twin of the automation system before physical installation, comprehensive verification and validation can be performed. Modern platforms offer core capabilities including:
- Synchronization of virtual and real-world line actions.
- Simulation of multi-robot I/O communication and signal logic.
- Advanced trajectory planning and collision detection for multi-axis systems.
In practice, this involves using engineering software for PLC program development and HMI configuration, significantly improving system interaction efficiency. Dedicated robotics software is used for offline programming and task simulation, substantially reducing on-site debugging risks and downtime. Combined with data acquisition software, real-time collection and analysis of multi-source equipment data provide actionable insights for production optimization and predictive maintenance, ensuring the line’s reliability for continuous electric car component production.
Overall Design of the Intelligent Machining Line for Electric Car Wheel Hubs
Addressing the specific needs of electric car wheel hub production, our design integrates mechanical, electrical, and software systems into a cohesive, automated line. The overall layout strategically combines industrial robots, CNC lathes, gear hobbing machines, 3D vision systems, roller conveyors, and precision positioning units to ensure seamless inter-process transition and full automation.
The line utilizes 4 GSK-RB35 robots, 4 CNC lathes, 1 gear hobbing machine, and a 3D vision system to cover the entire machining cycle. RFID technology is employed for automatic material identification and traceability. The operational workflow is a continuous, automated sequence:
- Intelligent Depalletizing: A 3D vision system guides a robot to identify and accurately grasp randomly stacked raw electric car wheel hub castings from a pallet.
- Precision Feeding: The robot places the hub onto a conveyor, which transports it to a secondary positioning station. Another robot then performs the automated loading of the CNC lathe, ensuring correct machining orientation.
- Synchronized Machining & Handling: Robots are programmed to service multiple machines. For instance, one robot manages the loading/unloading cycle for two lathes. After initial face machining, a dedicated flipping station re-orients the hub for machining the opposite side.
- In-Process Inspection: Critical dimensions, such as the bearing bore, are automatically measured post-turning. Conformant parts proceed; non-conformant parts are diverted to a quarantine area.
- Finish Processing: A robot transfers the qualified hub to a gear hobbing machine for final tooth cutting, followed by an automated pressure washing cycle.
- Automated Palletizing: The finished electric car wheel hub is picked by a robot and palletized according to a predefined, stable pattern for storage or shipment.
The entire line is governed by a central control system that provides real-time, visual production monitoring. All RFID and process data are fed into the Manufacturing Execution System (MES), supporting inventory management, dynamic scheduling, and closed-loop quality control for electric car component manufacturing.
This modular and flexible design allows for rapid changeover between different electric car wheel hub models via quick-change tooling and adaptive programs. The integrated data platform enables real-time monitoring and intelligent scheduling based on order priorities and resource availability, establishing a true foundation for high-quality, flexible manufacturing in the electric car industry.
Overall Control System Architecture
The control system for the electric car wheel hub line employs a hierarchical architecture, creating an efficient management loop through a Control Layer and a Production Execution Layer.
| System Layer | Core Component | Primary Function |
|---|---|---|
| Control Layer | GSK Line Control System | Unified scheduling and real-time control of robots, CNC machines, conveyors. Executes basic motions, collects sensor data, and manages process logic. |
| Production Execution Layer | Manufacturing Execution System (MES) | Manages production orders, detailed scheduling, quality tracking, product genealogy, and performance analysis. Acts as the information bridge between ERP and the shop floor. |
This structure ensures seamless communication: the MES decomposes high-level orders from the ERP system and dispatches detailed work instructions to the GSK control system, which then orchestrates the physical equipment to execute the plan.
Control and Production Execution Layers
The GSK control system forms the nervous system of the line. It continuously gathers data from sensors and measurement devices, processes it using efficient algorithms, and dispatches precise commands to all actuators. Through industrial network protocols and I/O interfaces, it maintains bidirectional communication with all devices, supporting remote commands, program management, and dynamic parameter adjustment, which is essential for the flexible production of various electric car models.
The MES drives the intelligent manufacturing model. By establishing unified data standards, it enables comprehensive information collection and interoperability across the entire electric car wheel hub production process. The synergistic, data-driven interaction between the MES and the control layer works in two directions:
- Bottom-Up Data Flow: Real-time equipment status, production counts, and quality data from the control layer are uploaded to the MES database.
- Top-Down Command Flow: Production schedules, process recipes, and quality thresholds from the MES are downloaded to the control layer for execution.
This closed-loop enables transparent management, real-time analytics for process optimization, and rapid response to demand fluctuations, ultimately enhancing the overall delivery capability for electric car components.
Control and Interaction Interface
To provide operators with comprehensive situational awareness, the system is equipped with HMI touch panels and large status display boards connected via Ethernet. These interfaces consolidate five core streams of information:
- Machine Status Dashboard: Real-time display of equipment utilization, job progress, and active alarms for quick anomaly response.
- MES Communication Status: Visual confirmation of data synchronization with the upper-level management system.
- Robotic Arm Operation: Live view of robot tasks, current trajectories, and operational states.
- Material Warehouse Status: Monitoring of raw material and finished goods inventory to prevent production stoppages.
- User Access Management: A tiered permission system to secure critical parameters and production data.
Key Technology Implementation and System Validation
Key Production Processes and Robotic Operations
The intelligence of the line is realized through the synchronized operation of multiple robots and their seamless integration with other automated stations. The eight key automated processes form a continuous, efficient flow specifically engineered for electric car wheel hubs. A detailed breakdown of the robot’s coordinated tasks is shown below, highlighting the multi-machine service strategy that maximizes throughput.
| Process Step | Robotic/Equipment Action | Objective & Outcome |
|---|---|---|
| 1. Vision-Guided Depalletizing | Robot uses 3D point cloud data to locate and grip a randomly oriented raw hub from a stack. | Solves the challenge of automating the feeding of unstructured raw materials, enabling true “lights-out” operation start. |
| 2. Precision Loading to CNC Lathe | Robot picks hub from conveyor after secondary positioning and loads it into Lathe 1 with micron-level repeatability. | Ensures perfect machining datum alignment, which is critical for the subsequent performance of the electric car wheel. |
| 3. Multi-Machine Synchronization | Robot 1 services Lathe 1 and Lathe 2. While Lathe 1 is machining, Robot 1 unloads/loads Lathe 2. | Dramatically reduces machine idle time. The cycle time bottleneck shifts from manual handling to the optimized machining cycle itself. |
| 4. Automated Flip & Second Operation | A dedicated flipping station re-orients the hub. Robot 2 then loads it into Lathe 3 for back-side machining. | Enables complete machining of complex electric car hub geometries without manual intervention. |
| 5. Coordinated Transfer & Inspection | Robot 2 transfers the hub from Lathe 3 to the in-line measuring station, and then to the next process based on the Go/No-Go result. | Integrates quality control directly into the flow, enabling immediate sorting and feedback. |
| 6. Gear Hobbing & Washing | Robot 3 loads the hub into the gear hobbing machine. After machining, it places the hub into an automated pressure washer. | Completes the final precision machining and ensures the product is clean for final inspection and assembly on the electric car. |
| 7. Final Automated Palletizing | Robot 4 picks the clean, finished hub and places it on an output pallet in a pre-programmed, stable pattern. | Prepares finished goods for shipment efficiently and safely, minimizing damage risk. |
The closed-loop integration of these processes has demonstrated a stable output of 500 high-quality electric car wheel hubs per day. Compared to the previous semi-automated method, this represents a quantifiable increase in production capacity of approximately 21.5%. This improvement stems directly from reduced cycle times, eliminated human delays, and optimal equipment utilization.
Implementation of the Vision Detection and Positioning System
The precision required for electric car components necessitates a meticulously calibrated vision system. For defect detection and guidance, we selected high-resolution CCD cameras. To achieve sub-pixel accuracy in edge detection for dimensional measurement, a multi-frame fusion algorithm is employed. This process reduces noise and enhances feature clarity. The positioning error for guiding the depalletizing robot is governed by the system’s calibration accuracy and can be modeled. The relationship between the robot’s world coordinate position $(X_r, Y_r, Z_r)$ and the vision system’s image coordinate detection $(u, v)$ is given by a transformation matrix derived during the Eye-to-Hand calibration process:
$$ \begin{bmatrix} X_r \\ Y_r \\ Z_r \\ 1 \end{bmatrix} = \mathbf{T} \cdot \begin{bmatrix} u \\ v \\ 1 \end{bmatrix} $$
where $\mathbf{T}$ is the $4 \times 3$ transformation matrix obtained through solving a least-squares problem using a set of known calibration points. The accuracy of this transformation is critical. After calibration with a standard calibration plate and verification using precision gauge blocks, the system’s measurement error was confirmed to be within $\pm0.05$ mm with a 95% confidence level, fully meeting the stringent quality control standards for electric car wheel hubs. This parameter set, fusing mechanical kinematics with machine vision, provides a replicable engineering solution for similar intelligent manufacturing applications.
PLC Logic Programming and Control Implementation
The programmable logic controller (PLC) is the workhorse executing the sequential and logical control of the entire electric car wheel hub line. Development within the TIA Portal platform followed structured programming principles. A standardized tag table was first created, defining all variables, their data types, and addresses—this is the foundation for stable and maintainable code. The core control logic was encapsulated within Function Blocks (FCs) for modularity, such as `FC_Conveyor_Control`, `FC_Gripper_IO`, and `FC_Vision_Trigger`. These blocks are called from a structured main organizational block (OB1) which manages the overall program cycle.
Key to the system’s flexibility is the implementation of distinct operational modes:
- Manual Mode: Allows operators to individually test and jog actuators, robots (via dedicated pendant), and conveyors for setup and maintenance.
- Automatic Mode: Executes the full, uninterrupted production cycle for the electric car hub.
- Single-Cycle Mode: Executes one complete work cycle and then stops, useful for debugging.
The handshake communication between the PLC and the four industrial robots was meticulously programmed. Using PROFINET or Ethernet/IP, the PLC sends commands (e.g., “Job 1 Execute,” “Gripper Open”) and receives status signals (e.g., “Job Complete,” “At Home Position”). This interlocking ensures safe, coordinated motion. The control logic heavily utilizes sequence control methods (graphically represented as SFC or implemented via step flags), which make the complex process flow for the electric car hub easy to visualize, troubleshoot, and modify.
Conclusion and Future Perspectives
The implemented intelligent machining line demonstrates a viable and effective pathway for the modernization of electric car component manufacturing. By integrating industrial robots, 3D vision inspection, in-process gauging, and a hierarchical control architecture, the line achieves significant gains in efficiency, consistency, and flexibility for electric car wheel hub production. The quantifiable improvement in output, coupled with enhanced quality assurance through automated inspection, provides a compelling case for such integrated systems.
The future evolution of such lines for the electric car industry is intrinsically linked to broader Industry 4.0 trends. The application of Artificial Intelligence and Machine Learning algorithms can move the system from automated to truly adaptive, predicting tool wear, optimizing robot paths in real-time, and dynamically adjusting schedules. The digital twin, used here for commissioning, can evolve into a living model for predictive maintenance and virtual what-if scenario testing. Furthermore, integrating green manufacturing principles—such as energy consumption monitoring of each station, swarf recycling systems, and optimized coolant management—will align production with the sustainable ethos of the electric car revolution. Continued research into advanced sensor fusion, cross-process adaptive control, and more sophisticated human-robot collaboration interfaces will further empower these production lines, providing a robust, intelligent, and sustainable technical backbone for the future of electric car manufacturing.