目的 针对航空发动机TA18钛合金薄壁管数控弯曲成形过程中强非线性、多参数耦合带来的回弹预测精度不足、工艺优化依赖试错等问题,开展弯曲回弹力学行为预测与变形补偿方法研究。方法 基于正交实验数据驱动,结合参数化有限元仿真,通过引入改进的克里金（Kriging）代理模型与主动学习算法,建立高效的回弹预测与补偿方法。首先通过正交实验分析各工艺参数对回弹的贡献,构建极差特征加权矩阵,并将其嵌入Kriging代理模型,从而在模型中有效融入物理先验知识。为降低参数空间边界区域的预测方差,建立参数化有限元仿真模型作为数据基础,并设计了一种基于方差最大化采样策略的主动学习框架,该框架能够自适应地在高不确定性区域补充样本。最后,开发了相应的回弹补偿算法,并开展了实验验证。结果 相比基于正交数据的线性回归模型,本文方法将TA18管材回弹预测的均方根误差（RMSE）从0.99°降低至0.50°,平均相对误差从2.40%降至0.83%,且计算效率较纯有限元模拟提升3个数量级。结论 本研究通过将物理先验知识与数据驱动主动学习融合,构建了TA18钛合金管弯曲回弹的高效预测补偿框架。该方法不仅解决了小样本条件下模型过拟合问题,还通过方差最大化采样优化了计算资源分配,为航空导管精密制造提供了兼顾精度与效率的技术路径。
Abstract
To address the challenges of insufficient springback prediction accuracy and the reliance on trial-and-error for process optimization caused by strong nonlinearity and multi-parameter coupling during the numerical control (NC) bending of aero-engine TA18 titanium alloy thin-walled tubes, the work aims to investigate methods for predicting bending springback mechanical behavior and compensating for deformation. An efficient springback prediction and compensation method was established based on orthogonal experiment data-driven approaches combined with parametric finite element (FE) simulation by incorporating an improved Kriging surrogate model and an active learning algorithm. Specifically, the contribution of each process parameter to springback was firstly analyzed via orthogonal experiments to construct a range feature weighting matrix. This matrix was then embedded into the Kriging surrogate model, effectively integrating physical prior knowledge into the modeling process. To reduce prediction variance at the boundaries of the parameter space, a parametric FE simulation model was established as the data foundation. Furthermore, an active learning framework based on a variance-maximization sampling strategy was designed to adaptively supplement samples in regions of high uncertainty. Finally, a corresponding springback compensation algorithm was developed and experimentally validated. Compared to the linear regression model based on orthogonal data, the method proposed in this work reduced the root mean square error (RMSE) of springback prediction for TA18 tube material from 0.99° to 0.50°, and the average relative error decreased from 2.40% to 0.83%. Additionally, the computational efficiency was improved by three orders of magnitude compared to pure FE simulation. This work establishes an efficient prediction and compensation framework for the bending springback of TA18 titanium alloy tubes by integrating physical prior knowledge with data-driven active learning. This method not only resolves the issue of model overfitting under small sample conditions but also optimizes computational resource allocation through variance-maximization sampling, providing a technical pathway for the precision manufacturing of aero-engine ducts that balances accuracy and efficiency.
关键词
TA18钛合金 /
克里金模型 /
主动学习 /
回弹预测 /
回弹补偿 /
数控弯曲
Key words
TA18 titanium alloy /
Kriging model /
active learning /
springback prediction /
springback compensation /
CNC bending
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基金
国家自然科学基金（12425205,12272075）
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