焊材用低温高锰钢的热变形行为与热加工图

doi:10.13251/j.issn.0254-6051.2025.03.035

摘要/Abstract

摘要： 采用Gleeble-3800热模拟试验机研究了焊材用低温高锰钢在温度800~1000 ℃、应变速率0.01~10 s^-1的热压缩变形行为,对试验应力-应变曲线进行了摩擦修正与温度修正。应用修正后的流变曲线建立了试验钢的Arrhenius本构方程及BP神经网络本构模型,并绘制了真应变为0.2、0.4和0.6的热加工图。结果表明,Arrhenius本构模型流变应力预测值与试验值的相关系数为0.976,基于BP神经网络构建的本构模型流变应力的预测精度更高,相关系数为0.996。试验低温高锰钢的最佳热加工区窗口为:变形温度930~1000 ℃、应变速率0.01~0.1 s^-1。结合显微组织分析,发现在变形温度950 ℃,应变速率0.01 s^-1工艺参数下,高锰钢能发生完全动态再结晶,获得均匀细小的等轴晶组织。

关键词: 低温高锰钢, 应力修正, 本构方程, 热加工图, 热变形行为

Abstract: Hot compression deformation behavior of a low-temperature high-Mn steel for welding materials was studied using a Gleeble-3800 thermal simulation testing machine within the temperature range of 800-1000 ℃ and strain rate range of 0.01-10 s^-1. Friction correction and temperature correction were applied to the stress-strain curves. The Arrhenius constitutive equation and the BP neural network constitutive model of the tested steel were established using the corrected flow curves. Additionally, hot processing maps at true strains of 0.2, 0.4, and 0.6 were plotted. The results show that the correlation coefficient between the predicted and tested values of the flow stress in the Arrhenius constitutive model is 0.976. The constitutive model based on the BP neural network has a higher prediction accuracy for the flow stress, with a correlation coefficient of 0.996. The optimal hot processing zone for the tested low-temperature high-Mn steel is as follows: the deformation temperature ranges from 930 ℃ to 1000 ℃, and the strain rate ranges from 0.01 s^-1 to 0.1 s^-1. Combined with the microstructure analysis, it is found that under the process parameters of a deformation temperature of 950 ℃ and a strain rate of 0.01 s^-1, complete dynamic recrystallization can occur in the tested steel, resulting in a uniform and fine equiaxed grain structure.

Key words: low-temperature high-Mn steel, stress correction, constitutive equation, hot processing map, hot deformation behavior

中图分类号:

TG142

杨婷, 潘进, 王程明, 马成, 佘亚东. 焊材用低温高锰钢的热变形行为与热加工图[J]. 金属热处理, 2025, 50(3): 220-226.

Yang Ting, Pan Jin, Wang Chengming, Ma Cheng, She Yadong. Hot deformation behavior and hot processing map of low-temperature high-Mn steel for welding materials[J]. Heat Treatment of Metals, 2025, 50(3): 220-226.

参考文献

[1] 杜青林, 亢淑梅, 李顺涛. 超低温高锰钢焊接技术发展及在LNG储罐中的应用[J]. 科技创新与应用, 2021(11): 40-43.
[2] 陈俊, 孟传峰, 孙超, 等. LNG储罐用高锰钢关键基础与技术开发[N]. 世界金属导报, 2019-11-24(B04).
[3] 陈亚魁. 超低温高锰钢焊接材料的研发: 熔敷金属力学性能和微观组织[D]. 武汉: 武汉科技大学, 2020.
[4] 杨江. 低温高锰钢焊接热影响区组织演变与力学性能研究[D]. 大连: 大连理工大学, 2021.
[5] 孟亮, 王红鸿, 陈亚魁, 等. LNG用高锰钢熔敷金属低温冲击韧性研究[J]. 电焊机, 2020, 50(11): 120-123.
Meng Liang, Wang Honghong, Chen Yakui, et al. Research on cryogenic impact toughness of deposited metals of high manganese steel for LNG[J]. Electric Welding Machine, 2020, 50(11): 120-123.
[6] 黄有林, 王建波, 凌学士, 等. 热加工图理论的研究进展[J]. 材料导报, 2008, 22(S3): 173-176.
Huang Youlin, Wang Jianbo, Ling Xueshi, et al. Research development of hot processing map theory[J]. Materials Reports, 2008, 22(S3): 173-176.
[7] 郭卜瑞, 徐佳炜, 刘世媛, 等. 40Cr钢热变形行为及热加工图[J]. 塑性工程学报, 2023, 30(2): 97-104.
Guo Burui, Xu Jiawei, Liu Shiyuan, et al. Hot deforming behavior and hot processing map of 40Cr steel[J]. Journal of Plasticity Engineering, 2023, 30(2): 97-104.
[8] 杨青云, 马博荣, 韩茂盛. 铝白铜合金热变形行为及热加工图的研究[J]. 热加工工艺, 2023, 52(19): 91-95.
Yang Qingyun, Ma Borong, Han Maosheng. Study on hot deformation behavior and hot processing map of aluminum white copper alloy[J]. Hot Working Technology, 2023, 52(19): 91-95.
[9] 彭力, 江健, 罗小峰, 等. TA18钛合金热变形行为及热加工图研究[J]. 钢铁钒钛, 2022, 43(6): 45-50.
Peng Li, Jiang Jian, Luo Xiaofeng, et al. Hot deformation behavior and processing maps of TA18 titanium alloy[J]. Iron Steel Vanadium Titanium, 2022, 43(6): 45-50.
[10] 李德君, 白强, 宋生印, 等. Fe-20Mn-3Si-3Al TRIP钢热变形行为及热加工图[C]//中国机械工程学会热处理分会第十一次全国热处理大会论文集. 2015: 1140-1144.
[11] 崔海涛, 董旭光, 邵忠财. Fe-20Mn-4Si-2Al高锰钢热加工性能研究[J]. 装备环境工程, 2018, 15(4): 1-4.
Cui Haitao, Dong Xuguang, Shao Zhongcai. Hot deformation behaviors of Fe-20Mn-4Al-2Si high-manganese steel[J]. Equipment Environmental Engineering, 2018, 15(4): 1-4.
[12] 王志蒙. 低碳高锰钢热变形行为研究[D]. 秦皇岛: 燕山大学, 2017.
[13] Puchi-Cabrera E S, Guérin J D, La Barbera-Sosa J G, et al. Friction correction of austenite flow stress curves determined under axisymmetric compression conditions[J]. Experimental Mechanics, 2019(3): 445-458.
[14] Ebrahimi R, Najafizadeh A. A new method for evaluation of friction in bulk metal forming[J]. Journal of Materials Processing Technology, 2004, 152(2): 136-143.
[15] 尚丽梅, 王春旭, 韩顺, 等. 基于摩擦-温度双修正的Maraging250钢热变形行为及热加工图[J]. 金属热处理, 2021, 46(5): 111-117.
Shang Limei, Wang Chunxu, Han Shun, et al. Hot deformation behavior and processing maps of Maraging250 steel based on friction and temperature double correction[J]. Heat Treatment of Metals, 2021, 46(5): 111-117.
[16] Li L, Zhou J, Duszczyk J. Determination of a constitutive relationship for AZ31B magnesium alloy and validation through comparison between simulated and real extrusion[J]. Journal of Materials Processing Technology, 2006, 172(3): 372-380.
[17] Sellars C M, Mctegart W J. On the mechanism of hot deformation[J]. Acta Metallurgica, 1966, 14(9): 1136-1138.
[18] 胡家齐, 王长军, 杨哲, 等. AM355不锈钢的热变形行为[J]. 金属热处理, 2020, 45(3): 50-59.
Hu Jiaqi, Wang Changjun, Yang Zhe, et al. Hot deformation behaviors of AM355 stainless steel[J]. Heat Treatment of Metals, 2020, 45(3): 50-59.
[19] 邹德宁, 韩彤, 张威, 等. 马氏体时效硬化不锈钢高温流变应力本构模型[J]. 塑性工程学报, 2018, 25(5): 248-253.
Zou Dening, Han Tong, Zhang Wei, et al. Constitutive model for high-temperature flow stress of martensitic age hardening stainless steel[J]. Journal of Plasticity Engineering, 2018, 25(5): 248-253.
[20] 林启权, 彭大暑, 朱远志. Al-Cu-Mg(2519)合金高温变形本构关系的神经网络模型[J]. 锻压技术, 2005, 30(1): 75-78.
Lin Qiquan, Peng Dashu, Zhu Yuanzhi. Neural net work model for the constitutive relationship of the Al-Cu-Mg(2519)aluminum alloy at elevated temperature[J]. Forging and Stamping Technology, 2005, 30(1): 75-78.
[21] 王俊峰, 何煜天. DH780钢的热变形行为及热加工图[J]. 金属热处理, 2020, 45(6): 114-119.
Wang Junfeng, He Yutian. Hot deformation behavior and hot processing map of DH780 steel[J]. Heat Treatment of Metals, 2020, 45(6): 114-119.
[22] Prasad Y V R K. Processing maps: A status report[J]. Journal of Materials Engineering and Performance, 2003, 12(6): 638-645.