Zhuojin Pipe Fitting Co., LtdJCO Precise Bending Forming Technology for Pipeline Steel Tubes
Literature Overview
The paper by Li Jian, Zhao Jun, and Ma Rui, published in Optics and Precision Engineering in 2010, presents a novel approach to precise bending forming in the JCO (Jog-Curl-Out) manufacturing process for pipeline steel tubes. The JCO process is a key technology for producing large-diameter welded pipes used in oil and gas pipeline construction. The primary challenge in JCO forming is controlling the ovality of the formed pipe blank, which directly affects the subsequent welding quality and pipe performance. This research introduces machine vision measurement technology combined with plastic bending engineering theory to achieve precise control of the bending process, reducing ovality to within 1.5% and forming angle monitoring error to within 0.2 degrees.
Core Technical Content
The JCO forming process involves three main stages: Jogging (pre-bending the plate ends), Curling (rolling the plate into a C-shape), and Opening (expanding the C-shape into a complete circle). The precise bending technology described in this paper focuses on the Curling stage, where the plate blank is progressively bent through multiple passes to achieve the target C-shape.
| Process Parameter | Conventional Method | Precise Bending Method | Improvement |
|---|---|---|---|
| Ovality control | 3.0-5.0% | ≤1.5% | 50-70% reduction |
| Forming angle error | 0.5-1.5° | ≤0.2° | 60-85% reduction |
| Process passes | Fixed number | Error-compensated iterative | Adaptive control |
| Operator dependence | High | Low | Process reliability improvement |
Two-Stage Pre-Bending Method
The first innovation in this research is the two-stage pre-bending method applied during the initial forming pass. By identifying the elastic springback behavior of the plate material through controlled pre-bending operations, the system can predict the precise pressing stroke required to achieve the target forming angle. This approach transforms the pre-bending from an empirical operation into a calculated, repeatable process.
The elastic springback behavior is characterized by the relationship between the applied bending moment and the resulting permanent deformation. For pipeline steel grades such as X65, X70, and X80, the springback angle depends on the yield strength, elastic modulus, plate thickness, and roll radius. The two-stage pre-bending method effectively calibrates this relationship in real-time for each production batch.
Error Compensation Technology
The second innovation is the error compensation technology applied during subsequent forming passes. After each forming pass, the actual forming angle is measured using machine vision, and the deviation from the target angle is calculated. This error is then compensated in the next forming pass by adjusting the pressing stroke accordingly. This iterative error compensation approach ensures that each pass achieves precise bending, accumulating minimal error over the entire forming sequence.
Machine Vision Measurement System
The machine vision system described in this paper is a critical enabler of the precise bending technology. The system includes:
- Image acquisition: A camera system captures images of the pipe blank end face during the forming process. The camera is positioned to provide a clear view of the forming angle at the pipe end.
- Image processing algorithm: A specialized algorithm is developed for pipe blank end face image processing, including preprocessing, edge detection, and straight line detection. The algorithm is optimized for the specific lighting conditions and surface characteristics of steel pipe blanks.
- Camera calibration: A novel calibration method using equilateral triangles as calibration templates is proposed. This method allows rapid and high-precision determination of camera external parameters from a single image, which is particularly advantageous for industrial environments where calibration time must be minimized.
- Angle conversion model: A mathematical model is established to convert the measured image angle to the actual forming angle of the pipe blank, accounting for camera distortion, perspective effects, and measurement uncertainties.
| Vision System Parameter | Specification |
|---|---|
| Measurement accuracy | ≤0.2° forming angle error |
| Calibration method | Single-image equilateral triangle template |
| Processing speed | Real-time (sufficient for production cycle) |
| Environmental adaptability | Industrial lighting, vibration, and dust conditions |
Engineering Practice Integration
The implementation of this precise bending technology in pipeline manufacturing offers several practical advantages:
- Quality improvement: Reduced ovality leads to improved welding quality, as the pipe ends are more uniformly shaped, resulting in better fit-up and more consistent weld bead profiles.
- Material efficiency: Precise forming reduces the need for post-forming correction operations, saving material and processing time.
- Process consistency: The machine vision-based control system provides consistent forming quality regardless of operator experience, reducing quality variation between shifts and production batches.
- Production efficiency: The error compensation approach allows for fewer forming passes to achieve the target geometry, potentially reducing cycle time.
For pipeline manufacturers, the adoption of this technology requires investment in machine vision equipment, software development, and operator training. However, the resulting quality improvements and process reliability justify the investment, particularly for high-grade pipeline applications where ovality specifications are stringent.
Study Insights and Reference Value
This research represents a significant advancement in pipeline manufacturing technology, combining classical plastic forming theory with modern measurement technology. The key insight is that precise control of the forming process requires not only accurate measurement but also intelligent process control that can adapt to material variations and process disturbances. The machine vision-based approach provides the measurement capability, while the error compensation algorithm provides the control intelligence. For the pipeline industry, this technology offers a pathway to achieving higher quality products with improved process consistency and reduced dependence on operator skill.