Zhuojin Pipe Fitting Co., LtdSignificance Analysis of Process Parameters on Wall Thickness Thinning in CNC Bending of 21-6-9 High-Strength Stainless Steel Pipe
Literature Overview
Fang Jun, Lu Shiqiang, Wang Kelu, and Yao Zhengjun from Nanjing University of Aeronautics and Astronautics and Nanchang Hangkong University conducted a systematic study on wall thickness thinning during computer numerical control (CNC) bending of 21-6-9 high-strength stainless steel pipe. Published in China Mechanical Engineering in 2015 under National Natural Science Foundation support (grant 51164030), this research addresses a critical forming quality issue in aerospace and high-performance structural applications.
Material Characteristics and Forming Challenges
The 21-6-9 high-strength stainless steel pipe possesses exceptional mechanical properties that simultaneously create challenging forming conditions:
| Property | Typical Value | Forming Implication |
|---|---|---|
| Yield strength | 960-1100 MPa | High forming force required |
| Elongation | 12-15% | Limited formability margin |
| Strain hardening exponent | Moderate | Work hardening accelerates thinning |
| Ductility ratio | Low | Early cracking risk under tension |
| Friction coefficient | Higher than austenitic grades | Increased material flow resistance |
The combination of high strength and limited ductility creates a narrow forming window where wall thickness thinning must be carefully controlled to prevent both excessive thinning and wrinkling defects.
Finite Element Modeling and Validation
A three-dimensional elastic-plastic finite element model was established using ABAQUS/Explicit for the CNC bending process. The model incorporated:
- Accurate material constitutive law capturing strain rate sensitivity
- Detailed contact definitions between pipe, mandrel, pressure blocks, and anti-wrinkle blocks
- Appropriate boundary conditions simulating the CNC bending machine kinematics
- Mesh convergence verification ensuring reliable strain calculations
The model was validated against experimental data, confirming its capability to predict wall thickness distribution with acceptable accuracy.
Orthogonal Experimental Design and Parameter Significance
The researchers employed orthogonal experimental design to systematically evaluate the influence of six process parameters on maximum wall thickness thinning rate. The significance ranking of parameters was determined as follows:
| Rank | Parameter | Direction of Effect on Thinning |
|---|---|---|
| 1 | Mandrel extension amount | Increases with increase |
| 2 | Pipe-mandrel clearance | Increases with decrease |
| 3 | Pipe-anti-wrinkle block friction coefficient | Increases with increase |
| 4 | Pipe-mandrel friction coefficient | Increases with increase |
| 5 | Pipe-pressure block friction coefficient | Increases with increase |
| 6 | Bending speed | Increases with increase |
A multiple linear regression model was established correlating maximum wall thickness thinning rate with the significant process parameters. The regression prediction model achieved relative errors not exceeding 5% when compared with orthogonal experimental results, demonstrating its reliability for process optimization purposes.
Engineering Practice Implications
For pipe bending operations in aerospace structures, pressure vessels, and high-performance piping systems, the findings provide actionable guidance:
- Mandrel design: Optimizing mandrel extension amount is the most effective strategy for controlling thinning, requiring careful consideration of the trade-off between thinning control and springback compensation.
- Clearance management: Tighter pipe-mandrel clearance reduces thinning but increases friction and forming force, necessitating a balanced design approach.
- Lubrication strategy: Reducing friction coefficients at all contact interfaces contributes to thinning control, with the anti-wrinkle block interface being particularly influential.
- Speed optimization: While bending speed has the lowest significance among the studied parameters, excessive speed should still be avoided to prevent thermal softening effects and die wear.
The regression model developed in this study provides a practical tool for process engineers to predict thinning behavior under various parameter combinations without resorting to time-consuming finite element simulations or physical trials, accelerating the process development cycle for new bending operations.