中国机械工程 ›› 2025, Vol. 36 ›› Issue (11): 2757-2765.DOI: 10.3969/j.issn.1004-132X.2025.11.034
• 先进材料加工工程 • 上一篇
周潼1(
), 程军2, 王克鲁1(
), 鲁世强1, 李鑫1, 刘杰1
收稿日期:2024-12-23
出版日期:2025-11-25
发布日期:2025-12-09
通讯作者:
王克鲁
作者简介:周潼,2000年生,男,硕士研究生。研究方向为增材制造工艺参数优化。E-mail: 1826646458@qq.com基金资助:
Tong ZHOU1(
), Jun CHENG2, Kelu WANG1(
), Shiqiang LU1, Xin LI1, Jie LIU1
Received:2024-12-23
Online:2025-11-25
Published:2025-12-09
Contact:
Kelu WANG
摘要:
通过等温恒应变速率压缩试验,研究了Ti2AlNb基合金在650~850 ℃、应变速率0.001~1 s-1范围内的热变形行为,并基于动态材料模型理论构建了三维加工图。分析了Ti2AlNb基合金的流动应力曲线,建立了支持向量机本构模型,再对三维加工图进行了理论分析,最后结合微观组织验证了所构建三维加工图的准确性。研究结果表明,Ti2AlNb基合金的流动应力随变形温度的降低和应变速率的增加而增大;支持向量机模型能准确预测Ti2AlNb基合金在不同变形工艺参数下的流动行为,其相关系数为0.999,平均相对误差为0.67%;三维加工图表明,功率耗散效率η值较大的区域集中在低应变速率区域;不同应变下Ti2AlNb基合金较好的热变形工艺参数范围为675~725 ℃、0.001~0.003 s-1,最佳热变形工艺参数为700 ℃、0.001 s-1。
中图分类号:
周潼, 程军, 王克鲁, 鲁世强, 李鑫, 刘杰. Ti2AlNb基合金热变形行为及加工图研究[J]. 中国机械工程, 2025, 36(11): 2757-2765.
Tong ZHOU, Jun CHENG, Kelu WANG, Shiqiang LU, Xin LI, Jie LIU. Hot Deformation Behavior and Processing Maps of Ti2AlNb-based Alloys[J]. China Mechanical Engineering, 2025, 36(11): 2757-2765.
图5 不同应变下Ti2AlNb基合金的功率耗散效率三维分布图
Fig.5 Three-dimensional distribution diagram of power dissipation efficiency for Ti2AlNb-based alloy under different strains
图6 不同工艺参数下Ti2AlNb基合金的功率耗散效率三维分布图
Fig.6 Three-dimensional distribution diagram of power dissipation efficiency for Ti2AlNb-based alloys under different processing parameters
图10 应变0.5、应变速率1 s-1、变形温度650 ℃下的失稳变形微观组织
Fig.10 Microstructure of the instability deformation under true strain of 0.5,strain rate of 1 s-1,deformation temperature of 650 ℃
| [1] | PENG Zhenlong, ZHANG Xiangyu, LIU Liangbao, et al. Effect of High-speed Ultrasonic Vibration Cutting on the Microstructure, Surface Integrity, and Wear Behavior of Titanium Alloy[J]. Journal of Materials Research and Technology 2023, 24: 3807-3888. |
| [2] | 曹子文, 朱明, 浦智能, 等. 烧结温度对Ti2AlNb合金显微组织及高温拉伸性能的影响[J]. 材料热处理学报, 2024, 45(8): 77-84. |
| CAO Ziwen, ZHU Ming, PU Zhineng, et al. Effect of Sintering Temperature on Microstructure and High Temperature Tensile Properties of Ti2AlNb Alloy[J]. Transactions of Materials and Heat Treatment,2024, 45(8): 77-84. | |
| [3] | 陈金坤, 姜凤阳, 思芳, 等. 二次烧结温度对放电等离子烧结制备Ti2AlNb合金显微组织和力学性能的影响[J]. 稀有金属与硬质合金, 2024, 52(4):54-58. |
| CEHN Jinkun, JIANG Fengyang, SI Fang, et al. Effect of Second-sintering Temperature on Microstructure and Mechanical Properties of Ti2AlNb Prepared by Spark Plasma Sintering[J]. Rare Metals and Cemented Carbides, 2024, 52(4):54-58. | |
| [4] | 马秀萍, 卜志强, 李然, 等. Ti2AlNb合金热模拟焊接热影响区的组织与力学性能[J]. 材料热处理学报, 2024, 45(2): 204-210. |
| MA Xiuping, PU Zhiqiang, LI Ran, et al. Microstructure and Mechanical Properties of Thermal Simulation Welding Heat-affected Zone of Ti2AlNb Alloy[J]. Transactions of Materials and Heat Treatment, 2024, 45(2): 204-210. | |
| [5] | 冯艾寒, 陈强, 王剑, 等. 低密度Ti2AlNb基合金热轧板微观组织的热稳定性[J]. 金属学报, 2023, 59(6): 777-786. |
| FENG Aihan, CHEN Qiang, WANG Jian, et al. Thermal Stability of Microstructures in Low-density Ti2AlNb-based Alloy Hot Rolled Plate[J]. Acta Metallurgica Sinica, 2023, 59(6): 777-786. | |
| [6] | 吴良, 陈铮. 基于支持向量机的材料热处理性能预测模型研究[J]. 材料热处理学报, 2007, 59(6): 152-155. |
| WU Liang, CHEN Zheng. Study on SVM Based Mathematical Model Used to Predict Mechanical Properties of Materials after Heat Treatment[J]. Transactions of Materials and Heat Treatment, 2007, 59(6): 152-155. | |
| [7] | LIU Limeng, CHU Maoxiang, GONG Rongfeng, et al. Unbalanced Classification Method Using Least Squares Support Vector Machine with Sparse Strategy for Steel Surface Defects with Label Noise[J]. Journal of Iron and Steel Research International, 2020, 27: 1407-1419. |
| [8] | 尹晓珊, 钟建琳, 彭宝营, 等. 基于麻雀算法优化的最小二乘支持向量机Ti2AlNb切削力预测研究[J]. 工具技术, 2023, 57(10): 63-68. |
| YIN Xiaoshan, ZHONG Jianlin, PENG Baoying,et al. Prediction of Ti2AlNb Cutting Force Based on Sparrow Algorithm Optimized Least Squares Support Vector Machine[J]. Tool Engineering, 2023, 57(10): 63-68. | |
| [9] | SIM K H, RI Y C, JO C H, et al. Modified Zerilli-Armstrong and Khan-Huang-Liang Constitutive Models to Predict Hot Deformation Behavior in a Powder Metallurgy Ti-22Al-25Nb Alloy[J].Vacuum, 2023, 210: 111749. |
| [10] | CHAI Yunpeng, ZHU Yanchun, QIN Ling, et al. High-temperature Hot Deformation Behavior and Processing Map of Ti-22Al-25Nb Alloy[J]. Materials Today Communications, 2024, 41: 110599. |
| [11] | PRASAD Y V R K, GEGEL H L, DORAIVELU S M, et al. Modeling of Dynamic Material Behavior in Hot Deformation: Forging of Ti-6242[J]. Metallurgical Transactions A,1984, 15(10): 1883-1892. |
| [12] | 刘晓燕, 张习祎, 陈秀全, 等. 热挤压态FGH96合金热变形行为及变形机制研究[J]. 稀有金属, 2024, 48(8): 1108-1119. |
| LIU Xiaoyan, ZHANG Xiwei, CHEN Xiuquan, et al. Hot Deformation Behavior and Deformation Mechanism of Hot Extruded FGH96 Superalloy[J]. Chinese Journal of Rare Metals, 2024, 48(8): 1108-1119. | |
| [13] | LI Chunhong, QIU Risheng, LUAN Baifeng, et al. Hot Deformation and Processing Maps of As-sintered CNT/Al-Cu Composites Fabricated by Flake Powder Metallurgy[J]. Transactions of Nonferrous Metals Society of China, 2018, 28(9): 1695-1704. |
| [14] | 周伟, 管峰涛, 钱新安, 等. 人工神经网络算法在粗晶Ti2AlNb合金本构关系模型建立中的应用[J]. 热加工工艺, 2023, 52(22): 107-109. |
| ZHOU Wei, GUAN Fengtao, QIAN Xin'an, et al. Application of Artificial Neural Network in Establishment of Constitutive Relationship Model of Coarse Grained Ti2AlNb[J]. Hot Working Technology, 2023, 52(22): 107-109. | |
| [15] | LIANG Shengli, ZHOU Meng, ZHANG Yi, et al. Thermal Deformation Behavior of GO/CeO2 In-situ Reinforced Cu30Cr10W Electrical Contact Material[J]. Journal of Alloys and Compounds, 2022, 899: 163266. |
| [16] | RANJAN A, MAHARAJA H, MISHRA S, et al. Microstructural Effects and Constitutive Modelling of Cyclic Softening Behaviour in Ti-6Al-4V Titanium Alloys[J]. Materials Science and Engineering A, 2024, 901: 146527. |
| [17] | 张志雄, 章俊涛, 韩建超, 等. 马氏体组织Ti-6Al-4V钛合金多向等温锻造组织演变及力学性能强化研究[J]. 中国机械工程, 2021, 32(22): 2739-2748. |
| ZHANG Zhixiong, ZHANG Juntao, HAN Jian-chao, et al. Microstructure and Mechanics Property Variations Duringmdlf of Ti-6Al-4V Alloy with a Martensitic Microstructure[J]. China Mechanical Engineering, 2021, 32(22): 2739-2748. | |
| [18] | GAO Wei, XU Fan, ZHOU Zhihua. Towards Convergence Rate Analysis of Random Forests for Classification[J]. Artificial Intelligence, 2022, 313: 103788. |
| [19] | ZHANG Tong. Statistical Behavior and Consistency of Classification Methods Based on Convex Risk Minimization[J]. The Annals of Statistics, 2004, 32(1): 56-85. |
| [20] | 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012. |
| LI Hang. Statistical Learning Method[M]. Beijing: Tsinghua University Press, 2012. | |
| [21] | TAKHANOV R. On the Speed of Uniform Convergence in Mercer's Theorem[J]. Journal of Mathematical Analysis and Applications, 2023, 518(2): 126718. |
| [22] | PAN Binbin, LAI Jianhuang, CHEN Wensheng. Nonlinear Nonnegative Matrix Factorization Based on Mercer Kernel Construction[J]. Pattern Recognition, 2011, 44(10/11): 2800-2810. |
| [23] | SUYKENS J A K, VANDEWALLE J. Least Squares Support Vector Machine Classifiers[J]. Neural Processing Letters, 1999, 9: 293-300. |
| [24] | WEN D X, LIN Y C, LI H B, et al. Hot Deformation Behavior and Processing Map of a Typical Ni-based Superalloy[J]. Materials Science and Engineering A, 2014, 591: 183-192. |
| [25] | MURTY S V S. N, RAO B N. On the Development of Instability Criteria during Hotworking with Reference to IN 718 [J]. Materials Science and Engineering: A, 1998, 254(1/2): 76-82. |
| [26] | DONG Xuemao, XU Jing, FENG Zhongxue, et al. Exploring Hot Deformation Behavior of the Solutionized Cu-15Ni-8Sn Alloy through Constitutive Equations and Processing Maps[J]. Journal of Materials Research and Technology, 2024, 29: 2142-2153. |
| [27] | 姜森宝, 王宇盛, 陈瑶, 等. Ti2AlNb钛合金轧板激光弯曲工艺及微观组织[J]. 锻压技术, 2024, 49(5): 61-66. |
| JIANG Senbao, WANG Yusheng, CHEN Yao, et al. Laser Bending Process and Microstructure for Ti2AlNb Titanium Alloy Rolled Sheet[J]. Forging & Stamping Technology, 2024, 49(5): 61-66. | |
| [28] | WU Ronghai, LIU Yang, GENG Cong, et al. Study on Hot Deformation Behavior and Intrinsic Workability of 6063 Aluminum Alloys Using 3D Processing Map[J]. Journal of Alloys and Compounds, 2017, 713: 212-221. |
| [29] | KONG Yonghua, CHANG Pengpeng, LI Qian, et al. Hot Deformation Characteristics and Processing Map of Nickel-based C276 Superalloy[J]. Journal of Alloys and Compounds, 2015, 622: 738-744. |
| [30] | LI Chunxiao, JIN Jianfeng, YAN Hong, et al. Investigation of Hot Deformation Behavior of Mg-14Gd-0.5 Zr (wt%) through Constitutive Analysis and Processing Maps[J]. Journal of Alloys and Compounds, 2023, 953: 170112. |
| [31] | 蔡述巧, 王勇帆, 陈克鑫, 等. 18Cr2Ni4W钢的热变形行为及热加工图[J]. 材料热处理学报, 2024, 45(7): 201-211. |
| CAI Shuqiao, WANG Yongfan, CHEN Kexin, et al. Hot Deformation Behavior and Hot Processing Map of 18Cr2Ni4W Steel[J]. Transactions of Materials and Heat Treatment, 2024,45(7): 201-211. | |
| [32] | 王斌, 张凯锋, 蒋少松, 等. 固溶温度对Ti2AlNb基合金组织演变的影响[J]. 航空材料学报, 2015, 35(3): 7-12. |
| WANG Bin, ZHANG Kaifeng, JIANG Shaosong, et al. Effect of Solution Treatment Temperature on Microstructural Evolution of Ti2AlNb-based Alloy[J]. Journal of Aeronautical Materials, 2015, 35(3): 7-12. |
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