Springback Prediction in Flexible 3D Hot Stretch Bending Based on Machine Learning

GAO Song; WANG Jun; XING Yasheng; SUN Yingli; SUN Xianping; LI Qihan; WANG Zonghao; WANG Guotao

doi:10.3969/j.issn.1674-6457.2026.05.022

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (5) : 239-247. DOI: 10.3969/j.issn.1674-6457.2026.05.022

Advanced Manufacturing Technology and Equipment

Springback Prediction in Flexible 3D Hot Stretch Bending Based on Machine Learning

GAO Song^1,*, WANG Jun¹, XING Yasheng², SUN Yingli¹, SUN Xianping¹, LI Qihan¹, WANG Zonghao¹, WANG Guotao¹

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Abstract

The work aims to employ machine learning methods to predict the springback amount in 3D hot stretch bending, addressing the computational complexity and intricate procedures of traditional analytical methods under complex process parameters. Firstly, a three-dimensional hot stretch bending experiment of TC4 titanium alloy was conducted to investigate the effect of different process parameters on forming quality. Subsequently, a 3D hot stretch bending numerical simulation model was established, and the accuracy of the numerical model was verified. Then, a parametric modeling approach was adopted to obtain a high-quality training dataset containing 2 430 samples with four parameters, including temperature, horizontal bending angle, vertical bending angle, and friction coefficient. Finally, two machine learning prediction models, eXtreme Gradient Boosting (XGBoost) and Random Forest (RF), were constructed. The prediction results were analyzed to demonstrate the accuracy of the prediction model and comparisons with experimental data were made to validate the effectiveness and stability of the model. The XGBoost algorithm achieved a coefficient of determination (R²) of 0.98 and a root mean square error (RMSE) of 0.55, while the random forest algorithm yielded an R² of 0.94 and an RMSE of 0.63. Analysis indicates that the XGBoost algorithm exhibits the highest prediction accuracy, with its prediction error for 3D stretch bending springback not exceeding 3.4%. This demonstrates that the XGBoost algorithm can achieve high-precision prediction of springback deformation in multi-point 3D hot stretch bending forming.

Key words

machine learning / 3D hot stretch bending / prediction model / TC4 titanium alloy / XGBoost

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GAO Song, WANG Jun, XING Yasheng, SUN Yingli, SUN Xianping, LI Qihan, WANG Zonghao, WANG Guotao. Springback Prediction in Flexible 3D Hot Stretch Bending Based on Machine Learning[J]. Journal of Netshape Forming Engineering. 2026, 18(5): 239-247 https://doi.org/10.3969/j.issn.1674-6457.2026.05.022

References

[1] FU K N, ZHAO Z W, PENG H L, et al.Manufacturing of Ultra-Thin Large Titanium Alloy Tube Using the Novel Hot Gas Pressure-Bending Process[J]. Journal of Materials Processing Technology, 2024, 326: 118358.
[2] QIAO Z, ZHAI R X, TIAN L J, et al.Springback Prediction and Control of TC4 Titanium Alloy Electrically Assisted Stretch-Bending Based on Electro-Thermal- Mechanical Coupling Constitutive Model[J]. The International Journal of Advanced Manufacturing Technology, 2025, 139(3): 1451-1465.
[3] SHAO Y L, LIU Z Z, DING H C, et al.Characterization of the Deformation Behavior and Microstructure Evolution in Laser Bending of TC4 Titanium Alloy Heavy Plate[J]. Journal of Laser Applications, 2024, 36(2): 022001.
[4] DENG X W, HUI S X, YE W J, et al.Numerical Simulation Research on Hot Stretch Straightening of Thin-Walled T-Shaped Profile of Titanium Alloy Assisted by Resistance Heating[J]. The International Journal of Advanced Manufacturing Technology, 2022, 121(9): 6483-6495.
[5] YANG X M, DANG L M, WANG Y Q, et al.Springback Prediction of TC4 Titanium Alloy V-Bending under Hot Stamping Condition[J]. Journal of Central South University, 2020, 27(9): 2578-2591.
[6] DENG R, XUE S, HE Q Q, et al. Research on the Effect of Quasi-β Forging Deformation on the Structure and Properties of TC21 Titanium Alloy[J]. Journal of Materials Engineering and Performance, 2025, 34(15): 15970-15979.[LinkOut]
[7] 高嵩, 于鹏, 梁继才, 等. 型材三维拉弯成形数值模拟及回弹特性研究[J]. 锻压技术, 2018, 43(6): 53-59.
GAO S, YU P, LIANG J C, et al. Research on Numerical Simulation and Springback Characteristic for 3D Stretch-Bending Parts[J]. Forging & Stamping Technology, 2018, 43(6): 53-59.[维普]
[8] 高嵩, 吴宇航, 李奇涵, 等. 铝型材三维拉弯成形回弹预测的理论解析与数值模拟[J]. 锻压技术, 2021, 46(4): 143-149.
GAO S, WU Y H, LI Q H, et al.Theoretical Analysis and Numerical Simulation on Springback Prediction for 3D Stretch Bending of Aluminum Profile[J]. Forging & Stamping Technology, 2021, 46(4): 143-149.
[9] 高嵩, 于长春, 梁继才, 等. 铝型材多点三维拉压复合弯曲成形工艺[J]. 机械工程学报, 2019, 55(20): 152-159.
GAO S, YU C C, LIANG J C, et al.Multi-Points 3D Stretch-Press Bending Technology for Aluminum Profile[J]. Journal of Mechanical Engineering, 2019, 55(20): 152-159.
[10] 惠生猛, 毛晓博, 湛利华. 铝锂合金回弹预测的机器学习及有限元仿真与实验[J]. 中国机械工程, 2024, 35(12): 2114-2121.
HUI S M, MAO X B, ZHAN L H.Machine Learning and Finite Element Simulation and Experimentation for Springback Prediction of Al-Li Alloys[J]. China Mechanical Engineering, 2024, 35(12): 2114-2121.
[11] 刘贵彬, 解小琴, 王平, 等. 15CrMoG钢管弯曲回弹的改进PSO-BP评估[J]. 机械设计与制造, 2023(10): 130-133.
LIU G B, XIE X Q, WANG P, et al.Improved PSO-BP Evaluation of 15CrMoG Steel Pipe Bending[J]. Machinery Design & Manufacture, 2023(10): 130-133.
[12] 王睿乾, 潘林, 童志远, 等. 航空发动机导管CNC弯曲回弹智能预测及补偿系统开发与应用[J]. 航空制造技术, 2024, 67(1): 106-111.
WANG R Q, PAN L, TONG Z Y, et al.Development and Application of Intelligent Prediction and Compensation System for Springback of Aero-Engine Pipes during CNC Bending[J]. Aeronautical Manufacturing Technology, 2024, 67(1): 106-111.
[13] 梁继才, 李义, 高嵩, 等. 型材多点柔性拉弯成形回弹预测[J]. 吉林大学学报(工学版), 2017, 47(1): 185-190.
LIANG J C, LI Y, GAO S, et al.Springback Prediction for Multi-Point 3D Stretch Bending Profile[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(1): 185-190.
[14] 陈光耀, 李恒, 贺子芮, 等. 基于机器学习的管材弯曲回弹有效预测与补偿[J]. 中国机械工程, 2020, 31(22): 2745-2752.
CHEN G Y, LI H, HE Z R, et al.Effective Prediction and Compensation of Springbacks for Tube Bending Using Machine Learning Approach[J]. China Mechanical Engineering, 2020, 31(22): 2745-2752.
[15] ZHANG S Y, FU M Y, WANG Z L, et al.Springback Prediction Model and Its Compensation Method for the Variable Curvature Metal Tube Bending Forming[J]. The International Journal of Advanced Manufacturing Technology, 2021, 112(11): 3151-3165.
[16] 占少伟, 龚俊杰, 韦源源, 等. 基于DPSO-BP神经网络的V形自由折弯成形角度和回弹预测[J]. 锻压技术, 2023, 48(8): 151-157.
ZHAN S W, GONG J J, WEI Y Y, et al.Prediction on V-Shaped Free Bending Angle and Springback Based on DPSO-BP Neural Network[J]. Forging & Stamping Technology, 2023, 48(8): 151-157.
[17] 王鹏鹏, 程子詹, 凌强, 等. 基于深度学习神经网络的铝合金型材回弹预测[J]. 锻压技术, 2024, 49(7): 105-111.
WANG P P, CHENG Z Z, LING Q, et al.Springback Prediction of Aluminum Alloy Profile Based on Deep Learning Neural Network[J]. Forging & Stamping Technology, 2024, 49(7): 105-111.
[18] CU S Y, SUN Y, WU K.Machine Learning Methods for Springback Control in Roll Forming[J]. International Journal of Material Forming, 2024, 18(1): 7.
[19] GAO X S, ZHANG K, ZHANG Z T, et al.XGBoost-Based Thermal Error Prediction and Compensation of Ball Screws[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2024, 238(1/2): 151-163.
[20] 钱尼君, 秦智军, 刘华龙, 等. 基于极端梯度提升算法的钢丝绳拉伸疲劳寿命预测[J]. 失效分析与预防, 2025, 20(3): 169-178.
QIAN N J, QIN Z J, LIU H L, et al.Tensile Fatigue Life Prediction of Steel Wire Ropes Based on Extreme Gradient Boosting Algorithm[J]. Failure Analysis and Prevention, 2025, 20(3): 169-178.
[21] TAO H F, CHEN R, XUAN J P, et al.Prioritization Analysis and Compensation of Geometric Errors for Ultra-Precision Lathe Based on the Random Forest Methodology[J]. Precision Engineering, 2020, 61: 23-40.
[22] 刘震锋, 陈建政, 赵春云, 等. 基于随机森林的钢轨轮廓在线识别方法研究[J]. 机械, 2024, 51(10): 52-58.
LIU Z F, CHEN J Z, ZHAO C Y, et al.Online Recognition Method of Steel Rail Profile Based on Random Forest Algorithm[J]. Machinery, 2024, 51(10): 52-58.
[23] SUTOU A, WANG J F.Influence-Balanced XGBoost: Improving XGBoost for Imbalanced Data Using Influence Functions[J]. IEEE Access, 2024, 12: 193473-193486.
[24] CHOI D K.Data-Driven Materials Modeling with XGBoost Algorithm and Statistical Inference Analysis for Prediction of Fatigue Strength of Steels[J]. International Journal of Precision Engineering and Manufacturing, 2019, 20(1): 129-138.
[25] 王孟超, 武川, 孟亚飞, 等. 基于机器学习模具钢大型锻件力学性能预测[J]. 精密成形工程, 2024, 16(12): 198-208.
WANG M C, WU C, MENG Y F, et al.Prediction of Mechanical Properties of Large Forgings in Mold Steel Based on Machine Learning[J]. Journal of Netshape Forming Engineering, 2024, 16(12): 198-208.

Funding

National Natural Science Foundation of China (51805045); Jilin Provincial Science and Technology Development Program (20240302115GX); Jilin Province Doctoral Student Support Program (BST202516)