journal article·2024-06

Corrosion fatigue life prediction method of aluminum alloys based on back-propagation neural network optimized by Improved Grey Wolf optimization algorithm

GaoFei Ji, ZhiPeng Li, LingHui Hu, HaoDong Huang, XianHai Song, Qiong Wu

Journal of Materials Science· Volume 59· Issue 23
Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Non-Destructive Testing Techniques · Advanced Machining and Optimization Techniques · Engineering Diagnostics and Reliability

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (42)

Show source-provided references
1. Pramod K, Abdul A, Prasad ACVS et al (2021) Study of Welding process parameter in TIG joining of Aluminum Alloy (6061). Mater Today: Proc 47(13):4020–4025
2. Zhang TY, Zhang T, He YT et al (2023) Corrosion and aging of organic aviation coatings: a review. Chin J Aeronaut 36(4):1–3510.1016/j.cja.2022.12.003 ↗
3. Xie LC, Ren SY, Yin F et al (2023) Effects of three stress levels on the fatigue properties and fracture mechanisms of an in-situ synthesized TiB/Ti-6Al-4V titanium matrix composite. Mater Charact 195:11251110.1016/j.matchar.2022.112511 ↗
4. Guo YC, Pan HM, Shen AQ et al (2023) Fracture properties of basalt-fiber-reinforced bridge concrete under dynamic fatigue loading. Structures 56:10501810.1016/j.istruc.2023.105018 ↗
5. Park SJ (2023) Application of ductile fracture model for the prediction of low cycle fatigue in structural steel. Eng Fract Mech 289:10946910.1016/j.engfracmech.2023.109469 ↗
6. Safar A, Jalali S, Silva L et al (2023) Effects of low cycle impact fatigue on the residual mode II fracture energy of adhesively bonded joints. Int J Adhes Adhes 126:10345510.1016/j.ijadhadh.2023.103455 ↗
7. Li W, Cheng LY, Zeng F et al (2023) High-temperature fatigue life improvement of small-deep holes by using a novel cold expansion process in a nickel-based superalloy. J Manuf Process 106:393–40610.1016/j.jmapro.2023.09.067 ↗
8. Li H, Luo X, Zhang YQ et al (2023) Viscoelastic fracture mechanics-based fatigue life model in asphalt-filler composite system. Eng Fract Mech 292:10958910.1016/j.engfracmech.2023.109589 ↗
9. Mohammad A, Khonsari M (2023) On the effect of environmental temperature on fracture fatigue entropy. Int J Fatigue 168:10741110.1016/j.ijfatigue.2022.107411 ↗
10. Zheng SK, Shen J, Wang W et al (2023) Multi-twinned deformation and fracture characteristics of directional solidified Ti-45.5Al–5Nb-0.5Ta alloys during high-temperature rotary-bending fatigue process. Mater Sci Eng A 876:14515710.1016/j.msea.2023.145157 ↗
11. Li A, Hu WP, Zhan ZX et al (2023) A novel continuum damage mechanics-based approach for thermal corrosion fatigue (TCF) life prediction of aluminum alloys. Int J Fatigue 163:10706510.1016/j.ijfatigue.2022.107065 ↗
12. Chen YJ, Liu CC, Zhou J et al (2019) Effect of alternate corrosion factors on multiaxial low-cycle fatigue life of 2024–T4 aluminum alloy. J Alloy Compd 772:1–1410.1016/j.jallcom.2018.08.282 ↗
13. Huang BH, Ye YX, Wang K et al (2023) Corrosion damage repair of 7075–T6 aluminum alloy by ultrasonic nanocrystal surface modification. Surf Coat Technol 474:13008510.1016/j.surfcoat.2023.130085 ↗
14. Chen NN, Liu QQ, Feng YL et al (2023) Microbial corrosion behavior and mechanism of 5A06 aluminum alloy under low dose proton radiation. J Market Res 27:4533–4540
15. Laurino A, Andrieu E, Harouard JP et al (2014) Effect of corrosion on the fatigue life and fracture mechanisms of 6101 aluminum alloy wires for car manufacturing applications. Mater Des 53:236–24910.1016/j.matdes.2013.06.079 ↗
16. Zhou J, Wang B, Liu CM et al (2023) Effect of static elastic stress on the corrosion behavior of 7A04 aluminum alloy exposed to real marine atmospheric environment. J Market Res 26:8197–8212
17. Sun ZC, Liang C, Chen Y et al (2023) Corrosion characteristics and prediction model of aluminum alloys in saturated Na2SO4 solution. Mater Chem Phys 308:12827310.1016/j.matchemphys.2023.128273 ↗
18. Huang YF, Ye XB, Hu BR et al (2016) Equivalent crack size model for pre-corrosion fatigue life prediction of aluminum alloy 7075–T6. Int J Fatigue 88:217–22610.1016/j.ijfatigue.2016.03.035 ↗
19. Zhong XC, Xie RK, Qin SH et al (2022) A process-data-driven BP neural network model for predicting interval-valued fatigue life of metals. Eng Fract Mech 276:10891810.1016/j.engfracmech.2022.108918 ↗
20. Wang YJ, Zhu ZY, Sha AX et al (2023) Low cycle fatigue life prediction of titanium alloy using genetic algorithm-optimized BP artificial neural network. Int J Fatigue 172:10760910.1016/j.ijfatigue.2023.107609 ↗
21. Reza MN, Nima S, Wenchen M et al (2022) Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network. Int J Fatigue 162:10697510.1016/j.ijfatigue.2022.106975 ↗
22. Jia YF, Fu R, Ling C et al (2023) Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime. Int J Fatigue 172:10764510.1016/j.ijfatigue.2023.107645 ↗
23. Xu LP, Zhang RL, Hao MQ et al (2023) A data-driven low-cycle fatigue life prediction model for nickel-based superalloys. Comput Mater Sci 229:11243410.1016/j.commatsci.2023.112434 ↗
24. Yu XR, Zhang GF, Jin HH et al (2023) A data driven model for estimating the fatigue life of 7075–T651 aluminum alloy based on the updated BP model. J Market Res 24:1252–1263
25. Song HP, Liu J, Zhang H et al (2023) Multi-source data driven fatigue failure analysis and life prediction of pre-corroded aluminum–lithium alloy 2050–T8. Eng Fract Mech 292:10962610.1016/j.engfracmech.2023.109626 ↗
26. Fan JL, Zhu G, Zhu ML et al (2023) A data-physics integrated approach to life prediction in very high cycle fatigue regime. Int J Fatigue 176:10791710.1016/j.ijfatigue.2023.107917 ↗
27. Zhou CJ, Wang HY, Hou SW et al (2023) A hybrid physics-based and data-driven method for gear contact fatigue life prediction. Int J Fatigue 175:10776310.1016/j.ijfatigue.2023.107763 ↗
28. Kashyzadeh KR, Ghorbani S (2023) New neural network-based algorithm for predicting fatigue life of aluminum alloys in terms of machining parameters. Eng Fail Anal 146:10712810.1016/j.engfailanal.2023.107128 ↗
29. Bagaria R, Wadhwani S, Wadhwani AK (2021) Bone fractures detection using support vector machine and error backpropagation neural network. Optik 247:16802110.1016/j.ijleo.2021.168021 ↗
30. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–6110.1016/j.advengsoft.2013.12.007 ↗
31. Yang Z, Qiao L, Su MK et al (2023) Ionospheric foF2 nowcast based on a machine learning GWO-ALSTM model. Adv Space Res 72(11):4896–491010.1016/j.asr.2023.09.028 ↗
32. Zhang QZ, Liu HS, Guo J et al (2023) Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment. Electr Power Syst Res 224:10975410.1016/j.epsr.2023.109754 ↗
33. Fan CC, Zheng YX, Wang SQ et al (2023) Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model. Constr Build Mater 400:13260210.1016/j.conbuildmat.2023.132602 ↗
34. Sebayang AH, Kusumo F, Milano J et al (2023) Optimization of biodiesel production from rice bran oil by ultrasound and infrared radiation using ANN-GWO. Fuel 346:12840410.1016/j.fuel.2023.128404 ↗
35. Wang JG, Xu XF, Xu Y et al (2023) Fouling prediction of heat exchanger surface under alternating magnetic field based on IGWO–SVR. Int J Therm Sci 184:10801810.1016/j.ijthermalsci.2022.108018 ↗
36. Song HQ, Ma WG, Han ZH et al (2022) Diagnosis of unbalanced rolling bearing fault sample based on adaptive sparse contrative Auto-encoder and IGWO-USELM. Measurement 198:11135310.1016/j.measurement.2022.111353 ↗
37. Lin J, Zhang SX, Zheng SY et al (2022) Differential evolution with fusion of local and global search strategies. J Comput Sci 63:10174610.1016/j.jocs.2022.101746 ↗
38. Moslemzadeh M, Farzin S, Karami H et al (2023) Introducing improved atom search optimization (IASO) algorithm: Application to optimal operation of multi-reservoir systems. Phys Chem Earth 131:10341510.1016/j.pce.2023.103415 ↗
39. Wang ZD, Gao XG, Tan XY et al (2021) Determining the direction of the local search in topological ordering space for Bayesian network structure learning. Knowl-Based Syst 234:10756610.1016/j.knosys.2021.107566 ↗
40. Yu Y, Zhang TF, Lei ZY et al (2023) A chaotic local search based LSHADE with enhanced memory storage mechanism for wind farm layout optimization. Appl Soft Comput 141:11030610.1016/j.asoc.2023.110306 ↗
41. Tang CZ, Li HW, Li KS et al (2024) Data-driven fatigue life prediction of small-deep holes in a nickel-based superalloy after a cold expansion process. Int J Fatigue 181:10805910.1016/j.ijfatigue.2024.108159 ↗
42. Pan XM, Liu JH, Li YT et al (2024) Fatigue behavior analysis and life evaluation method of building steel under the influence of multiple factors. Eng Fract Mech 303:11011610.1016/j.engfracmech.2024.110116 ↗