考虑高强度钢加工表面完整性的背应力能法疲劳寿命预测模型

doi:10.12382/bgxb.2021.0828

摘要/Abstract

摘要：

循环应变能法从疲劳损伤机理的角度对疲劳寿命进行预测，能够很好地解释疲劳断裂行为，故得到广泛应用。然而，对材料整体性能的大量试验研究中常常不考虑加工表面完整性，从而影响了能量法的预测精度，为此提出一种考虑加工表面完整性的循环背应力能法疲劳寿命预测修正模型，通过引入残余应力随深度围成的面积作为影响因子来考虑加工表面层力学特征对总背应力能的影响，将微裂纹不扩展阈值作为影响因子表征表面层几何和组织特征对总背应力能的作用。研究结果表明：针对淬火回火前的重载车辆扭力轴高强度钢材料，考虑加工表面完整性的修正模型疲劳寿命预测误差分散带缩小38%，预测精度提高25%，与疲劳断裂后实时统计单周次循环背应力能密度的模型具有相同的误差分散带(1.25倍)，克服了传统能量法需要在疲劳试验后才能进行寿命预测的缺点；针对淬火回火后的超高强度钢材料，考虑加工表面完整性的修正模型疲劳寿命预测精度从72.7%提升到了92.2%，克服了疲劳断裂后实时统计单周次循环背应力能密度预测模型的失效性，误差分散带从3.30倍降到了1.41倍；新模型提高了能量法在不同加工表面层特征的适用性，实现了面向服役性能的加工表面完整性评价。

关键词: 加工表面层, 背应力能, 疲劳寿命预测, 高强度钢

Abstract:

The cyclic strain energy method has been widely used because it can predict fatigue life from the perspective of fatigue damage mechanism and explain many fatigue phenomena which cannot be explained by other methods. However, in the large amount of empirical study of the overall properties of materials, the machined surface layer is often not considered, which affects the prediction accuracy of the energy method. Based on the energy method, a new fatigue life prediction model with back stress energy based on the machined surface layer of high strength steel was proposed. The effect of surface mechanical characteristics on total back stress energy was considered by introducing the area of residual stress with depth as the influence factor, while the effect of surface layer geometry and metallurgy on strain energy was considered by taking the non-propagation threshold of microcrack as the influence factor. The results showed that: the dispersion band of fatigue life prediction error for the modified model considering surface integrity was reduced by 38% and the prediction accuracy improved by 25%; it had the same error dispersion band (1.25 times) as the model for the real-time statistical single cycle energy density after fatigue fracture, which overcomes the disadvantage that the fatigue life can be predicted only after the fatigue test of the traditional energy method; for quenched and tempered high strength steel, the average accuracy of fatigue life prediction of the modified model considering surface integrity was improved from 72.7% to 90.6%; compared with the real-time statistics after fatigue fracture, the error dispersion band of the single cycle energy density prediction model was reduced from 3.30 times to 1.41 times. This model provides a method to assess the fatigue service performance of the machined surface layer, and improves the applicability of strain energy in different machined surface layer characteristics.

Key words: machined surface layer, back stress energy, fatigue life prediction, high strength steel

王永, 王西彬, 王志斌, 刘志兵, 刘书尧, 陈洪涛, 王湃. 考虑高强度钢加工表面完整性的背应力能法疲劳寿命预测模型[J]. 兵工学报, 2023, 44(3): 806-815.

WANG Yong, WANG Xibin, WANG Zhibin, LIU Zhibing, LIU Shuyao, CHEN Hongtao, WANG Pai. Fatigue Life Prediction Model with Back Stress Energy Method Based on the Machined Surface Layer of High Strength Steel[J]. Acta Armamentarii, 2023, 44(3): 806-815.

图/表 14

参考文献 21

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工艺	加工参数
T1	n=1200 r/min; a_p=0.5 mm; f=0.35 mm/r，r_c=0.8 mm
T2	n=2 037 r/min，a_p=0.25 mm，f=0.12 mm/r，干式，r_c=0.4 mm
T3	n=2 037 r/min，a_p=0.25 mm，f=0.12 mm/r，湿式，r_c =0.4 mm
G1	n=150 r/min，v=25 m/s，a_p=0.01 mm，f=200 mm/min

工艺	加工参数
T1	n=1200 r/min; a_p=0.5 mm; f=0.35 mm/r，r_c=0.8 mm
T2	n=2 037 r/min，a_p=0.25 mm，f=0.12 mm/r，干式，r_c=0.4 mm
T3	n=2 037 r/min，a_p=0.25 mm，f=0.12 mm/r，湿式，r_c =0.4 mm
G1	n=150 r/min，v=25 m/s，a_p=0.01 mm，f=200 mm/min

工艺	几何特征				组织特征		力学特征	疲劳寿命
工艺	R_a/μm	R_y/μm	R_z/μm	R_sm/μm	疲劳前硬度/ HV	疲劳后硬度/ HV	σ_res/MPa	寿命/周次	平均寿命/周次
T11	3.191	20.09	10.685	23.239	374	449	243	6 056	5 704
T12	3.513	20.577	10.968	44.068	371	453	248	4 917
T13	3.381	32.023	18.891	31.695	381	446	251	6 138
T21	5.015	20.541	12.477	20.237	353	390	244	5 867	6 854
T22	5.209	35.896	19.96	12.069	348	392	305	8 180
T23	5.929	61.611	25.513	17.339	357	387	278	6 514
T31	1.272	8.709	7.482	11.459	369	421	332	9 032	8 547
T32	1.29	8.091	6.919	11.843	375	426	242	8 538
T33	1.273	9.761	7.708	10.544	381	431	208	8 071
G41	0.902	25.096	13.859	3.245	348	364	-266	14 117	13 950
G42	1.13	21.841	16.352	3.173	339	371	-234	16 318
G43	1.584	23.949	20.086	3.125	342	374	-248	11 416

工艺	几何特征				组织特征		力学特征	疲劳寿命
工艺	R_a/μm	R_y/μm	R_z/μm	R_sm/μm	疲劳前硬度/ HV	疲劳后硬度/ HV	σ_res/MPa	寿命/周次	平均寿命/周次
T11	3.191	20.09	10.685	23.239	374	449	243	6 056	5 704
T12	3.513	20.577	10.968	44.068	371	453	248	4 917
T13	3.381	32.023	18.891	31.695	381	446	251	6 138
T21	5.015	20.541	12.477	20.237	353	390	244	5 867	6 854
T22	5.209	35.896	19.96	12.069	348	392	305	8 180
T23	5.929	61.611	25.513	17.339	357	387	278	6 514
T31	1.272	8.709	7.482	11.459	369	421	332	9 032	8 547
T32	1.29	8.091	6.919	11.843	375	426	242	8 538
T33	1.273	9.761	7.708	10.544	381	431	208	8 071
G41	0.902	25.096	13.859	3.245	348	364	-266	14 117	13 950
G42	1.13	21.841	16.352	3.173	339	371	-234	16 318
G43	1.584	23.949	20.086	3.125	342	374	-248	11 416

工艺	几何特征/μm				组织特征		力学特征	疲劳寿命
工艺	R_a/μm	R_y/μm	R_z/μm	R_sm/μm	疲劳前硬度/HV	疲劳后硬度/HV	σ_res/MPa	寿命/周次	平均寿命/周次
T11	3.4	17.2	11.0	49.0	558	589	-346	13558	11585
T12	3.42	16.8	11.4	48.7	548	576	-326	9611	11585
G11	2.0	18.1	16.3	12.5	570	574	-126	5277	5659
G12	2.20	17.9	16.1	12.2	562	564	-137	6040	5659
G21	1.7	23.9	16.2	15.2	485	584	-223	7613	6627
G22	1.72	23.9	16.0	14.9	470	588	-225	5641	6627
G31	2.0	17.7	14.8	16.5	571	562	-183	6096	5896
G32	2.2	16.3	14.1	17.2	574	558	-179	5696	5896