Ti6554钛合金低温力学性能及机器学习预测模型

刘传焜; 曲周德; 武川

doi:10.3969/j.issn.1674-6457.2026.04.002

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精密成形工程 ›› 2026, Vol. 18 ›› Issue (4) : 11-23. DOI: 10.3969/j.issn.1674-6457.2026.04.002

轻合金成形

Ti6554钛合金低温力学性能及机器学习预测模型

刘传焜, 曲周德^*, 武川

作者信息 +

Cryogenic Mechanical Properties and Machine Learning Prediction Model of Ti6554 Titanium Alloy

LIU Chuankun, QU Zhoude^*, WU Chuan

Author information +

文章历史 +

摘要

目的研究Ti6554钛合金在低温环境下的力学行为及组织演变,揭示温度对其力学性能的影响机制。方法利用液氮结合拉伸实验获取载荷-位移曲线,并通过真应力-真应变分析、断口观察、加工硬化率测定及EBSD表征进行深入分析,同时构建了支持向量回归-粒子群优化算法模型（PSO-SVR）和EBP神经网络预测模型。结果随着温度降低,合金应力显著升高,孪晶与位错的交互作用强化了屈服强度但导致延伸率下降,在0~-180 ℃区间内,断裂方式由韧性断裂逐渐转变为韧脆性混合断裂。模型精度评估结果表明,PSO-SVR模型最优R²为0.94,RMSE平均值为11.12 MPa,MAE平均值达到9.024 MPa;EBP模型最优R²高达0.98,RMSE的平均值为4.01 MPa,MAE平均值为2.067 MPa,展现的性能更优。结论低温显著提升了材料的屈服强度与抗拉强度,其根本原因在于原子间结合力增强,导致位错滑移需克服的晶格摩擦阻力更高,同时原子热运动减弱,使位错难以获得足够能量克服障碍。然而,这种强化伴随塑性下降、延伸率降低。随着拉伸温度从0 ℃降低至-180 ℃,合金的主导断裂机制由韧性断裂转变为韧脆性混合断裂。EBP神经网络预测模型预测精度显著优于支持向量回归-粒子群优化算法模型（PSO-SVR）的预测精度。

Abstract

The work aims to study the mechanical behavior and microstructural evolution of Ti-6554 titanium alloy subject to cryogenic conditions, elucidating the underlying mechanisms governing temperature-dependent property variations. Load-displacement curves were captured via a liquid nitrogen-integrated tensile testing apparatus, while temperature-dependent mechanisms governing mechanical properties were probed through comprehensive analysis of true stress-strain curves, fractographic observations, work hardening rate quantification, and electron backscatter diffraction (EBSD) characterization. Based on these findings, a Support Vector Regression optimized by Particle Swarm Algorithm (PSO-SVR) and an Error Back-Propagation neural network model (EBP) were formulated. Experimental outcomes revealed that progressive temperature reduction induced significant flow stress elevation of Ti-6554 titanium alloy, where twin-dislocation interactions were found to enhance yield strength yet diminish elongation. Crucially, a fracture mode transition from ductile to ductile-brittle mixed failure was documented across the 0 ℃ to -180 ℃ regime. Model validation demonstrated optimal R² values of 0.94 and 0.98 for PSO-SVR and EBP respectively, with corresponding mean RMSE of 11.12 MPa and 4.01 MPa, and mean MAE of 9.024 MPa and 2.067 MPa, conclusively establishing the superior predictive capability of the EBP framework. Cryogenic temperatures substantially enhance both the yield strength and ultimate tensile strength of the material. This strengthening is fundamentally attributed to increased interatomic bonding forces, which elevates the lattice friction resistance that dislocation slip must overcome, coupled with the diminished thermal activation energy available for dislocations to surmount obstacles. However, this strengthening is accompanied by degradation in plasticity, manifested as reduced elongation. As the tensile temperature decreases from 0 ℃ to -180 ℃, the alloy's dominant fracture mechanism shifts from ductile fracture to a ductile-brittle mixed fracture. The developed EBP neural network prediction model demonstrates significantly superior predictive accuracy compared to the PSO-SVR model.

导出引用

刘传焜, 曲周德, 武川. Ti6554钛合金低温力学性能及机器学习预测模型[J]. 精密成形工程. 2026, 18(4): 11-23 https://doi.org/10.3969/j.issn.1674-6457.2026.04.002

LIU Chuankun, QU Zhoude, WU Chuan. Cryogenic Mechanical Properties and Machine Learning Prediction Model of Ti6554 Titanium Alloy[J]. Journal of Netshape Forming Engineering. 2026, 18(4): 11-23 https://doi.org/10.3969/j.issn.1674-6457.2026.04.002

中图分类号： TG115.5+2 TG146.2

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