Fatigue Performance Prediction Study of Al-Li Alloy-based on Experimental and “Shallow” + “Deep” Hybrid Neural Network Methods

doi:10.3901/JME.2024.16.190

Abstract

Abstract: With its excellent mechanical properties, aluminum-lithium alloy has increasingly important in aerospace field and is one of the most rapidly developing lightweight materials. Fatigue and fracture are one of the main causes of failure of aerospace structural components, and fatigue damage is highly uncertain and sudden, so its fatigue performance evaluation and prediction has become a hot research topic. The fatigue experiments of 2A97 aluminum-lithium alloy considering the sampling direction, notch, etc. are carried out to obtain eight sets of complete S-N curves and the influence of relevant factors on the fatigue performance of aluminum-lithium alloy are analyzed. A hybrid neural network model based on shallow network and deep learning is innovatively proposed. The shallow algorithm is used to train the low cycle fatigue experimental data to realize data derivation at first, and then accurately predict the fatigue limit of aluminum-lithium alloy under different working conditions by deep learning. This method provides a new way for the research of fast and accurate evaluation of fatigue properties of materials.

Key words: aluminum-lithium alloy, fatigue performance, shallow networks, deep learning

CLC Number:

O346

ZHAO Dewang, JIANG Chao, ZHAO Yanguang, YANG Wenping, FAN Junling. Fatigue Performance Prediction Study of Al-Li Alloy-based on Experimental and “Shallow” + “Deep” Hybrid Neural Network Methods[J]. Journal of Mechanical Engineering, 2024, 60(16): 190-199.

References

[1] 徐佳辉,黄亮,谢冰鑫,等. 铝锂合金电磁形变复合热处理工艺研究[J]. 机械工程学报,2022,58(16):58-67. XU Jiahui, HUANG Liang, XIE Bingxin,et al. Study on electromagnetic deformation combined with heat treatment process of Al-Li alloy[J]. Journal of Mechanical Engineering,2022,58(16):58-67.
[2] 李飘,姚卫星. 铝锂合金材料发展及综合性能评述[J].航空工程进展,2019,10(1):12-20. LI Piao,YAO Weixing. Review on the development and comprehensive properties of Al-Li alloy materials[J]. Advances in Aeronautical Science and Engineering,2019,10(1):12-20.
[3] DI Z,SAJI S,HORI S. Effect of microstructure on high cycle fatigue behaviour of AL-Li binary alloy[J]. Le Journal de Physique Colloques,1987,48(3):753-759.
[4] ALEXOPOULOS N D,MIGKLIS E,STYLIANOS A,et al. Fatigue behavior of the aeronautical Al-Li(2198) aluminum alloy under constant amplitude loading[J]. International Journal of Fatigue,2013,56:95-105.
[5] 艾素华,吴细毛,张匀. S'和T₂相对8090铝锂合金低周疲劳行为的影响[J]. 机械工程材料,1998,22(2):6-9. AI Suhua,WU Ximao,ZHANG Yun. Effected of precipitated phase S' and T₂ on the low cycle fatigue behaviour of 8090 Al-Li alloy[J]. Materials for Mechanical Engineering,1998,22(2):6-9.
[6] 郝敏,王亮,陈军洲,等. 2060-T8E30铝锂合金平面各向异性和断裂破坏机制研究[J]. 稀有金属,2021,45(6):641-649. HAO Min,WANG Liang,CHEN Junzhou,et al. Research on 2060-T8E30 Aluminum-Lithium alloy plane anisotropy and fracture failure mechanism[J]. Chinese Journal of Rare Metals,2021,45(6):641-649.
[7] 张雪洋,钟振东,潘雪纯,等. 划痕损伤对含铆钉孔2198-T8铝锂合金疲劳性能的影响[J]. 塑性工程学报,2022,29(9):199-206. ZHANG Xueyang,ZHONG Zhendong,PAN Xuechun,et al. Effect of scratch damage on fatigue performance of 2198-T8 aluminum-lithium alloy with rivet holes[J].Journal of Plasticity Engineering,2022,29(9):199-206.
[8] 杨文平,郭杏林,赵延广. 对流和辐射换热对金属高周疲劳能量耗散估计的影响[J]. 机械工程学报,2021,57(10):187-195. YANG Wenping,GUO Xinglin,ZHAO Yanguang. Effects of convection and radiation heat transfer on energy dissipation estimation of metal in high-cycle fatigue[J]. Journal of Mechanical Engineering,2021,57(10):187-195.
[9] RAMACHANDRA S,DURODOLA J F,FELLOWS N A,et al. Experimental validation of an ANN model for random loading fatigue analysis[J]. International Journal of Fatigue,2019,126:112-121.
[10] FENG C,XU L Y,ZHAO L,et al. Prediction of welded joint fatigue properties based on a novel hybrid SPDTRS-CS-ANN method[J]. Engineering Fracture Mechanics,2022,275:108824.
[11] BRITO O G A,FREIRE J R C S,CONTE M V L A,et al. A hybrid ANN-multiaxial fatigue nonlocal model to estimate fretting fatigue life for aeronautical Al alloys[J]. International Journal of Fatigue,2022,162:107011.
[12] ZHANG X,GONG J G,XUAN F Z. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions[J]. International Journal of Fatigue,2021,148:106236.
[13] YANG Jingye,KANG Guozheng,LIU Yujie,et al. A novel method of multiaxial fatigue life prediction based on deep learning[J]. International Journal of Fatigue,2021,151:106356.
[14] 王刚,高琛,王书艳,等. 频率及应力比对18CrNiMo7-6合金钢疲劳性能的影响[J]. 热加工工艺,2022,51(2):51-55. WANG Gang,GAO Chen,WANG Shuyan,et al. Effects of frequency and stress ratio on fatigue performance of 18CrNiMo7-6 alloy steel[J]. Hot Working Technology,2022,51(2):51-55.
[15] MANNO A,MARTELLI E,AMALDI E. A shallow neural network approach for the short-term forecast of hourly energy consumption[J]. Energies,2022,15:958.
[16] XIAO Z,YE S J,ZHONG B,et al. BP neural network with rough set for short term load forecasting[J]. Expert Systems with Applications,2009,36(1):273-279.
[17] ZHAO Dewang,ZHAO Kunmin,REN Daxin,et al. Ultrasonic welding of magnesium-titanium dissimilar metals:A study on influences of welding parameters on mechanical property by experimentation and artificial neural network[J]. Journal of Manufacturing Science and Engineering-Transactions of the ASME,2017,139(3):031019.
[18] ZHAO Dewang,REN Daxin,ZHAO Kunmin,et al. Effect of welding parameters on tensile strength of ultrasonic spot welded joints of aluminum to steel-By experimentation and artificial neural network[J]. Journal of Manufacturing Processes,2017,30(12):63-74.
[19] ZHAO Dewang,REN Daxin,SONG Gang,et al. Comparison of mechanical properties and the nugget formation of composite ceramic-centered annular welding and traditional resistance spot welding[J]. International Journal of Mechanical Sciences,2020,187(5):105933.
[20] GERS F A,SCHMIDHUBER E. LSTM recurrent networks learn simple context-free and context-sensitive languages[J]. IEEE Transactions on Neural Networks,2001,12(6):1333-1340.