Virtual vibration test rig for fatigue analysis of dozer push arms

Abstract

To obtain accurate fatigue life results for construction machinery components, acquiring load spectra is crucial, as their authenticity and validity directly determine the precision of the analysis. In working conditions, component attitudes change continuously, but they remain static on the vibration test rig (VTR), so the acquired target signals should match with the actual component attitudes in the driving signal generation. This paper proposes an efficient and economical simulation-based virtual VTR for fatigue analysis of dozers. First, the relationship between the push arm rotation angle and the cylinder stroke is established, since the cylinder strokes can be measured easily in data acquisition experiments. Second, load decomposition is used to determine the attitude relationship between virtual VTR conditions and actual conditions, and target signals are calculated based on this attitude relationship and measured data. According to the system’s frequency response function, the driving signals are iterated until the system’s response signals converge with the target signals. Finally, the iteratively obtained load spectra are utilized for fatigue life analysis. The results show that the virtual VTR can effectively and accurately obtain the results of fatigue analysis and has engineering application significance.

Keywords

virtual vibration test rig / driving signal generation / component attitudes / fatigue analysis / dozer push arm

References

[1] Liu G, Luo G, Zhang Z, Yang X, Wu D. Fatigue life analysis and structure performance improvement of bulldozer working device based on ANSYS. Paper presented at:3rd IEEE Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 3-5 October 2017. 2017.

[2] Qin C, Ma C, Li L, Sun X, Liu Z, Sun Z. Development and application of an intelligent robot for rock mass structure detection: a case study of Letuan tunnel in Shandong, China. Int J Rock Mech Min Sci. 2023;169:105419.

[3] Kogan J. On the durability analysis of machinery components. Int J Fatigue. 1981;3:27-30.

[4] Beretta S, Clerici P. A novel test rig for narrow band random fatigue under rotating bending. Int J Fatigue. 1997;19:457-460.

[5] Gao N, Brown M, Miller K, Reed P. An effective method to investigate short crack growth behaviour by reverse bending testing. Int J Fatigue. 2007;29:565-574.

[6] Koch U, Wiedemann D, Ulbrich H. Model-based MIMO state-space control of a car vibration test rig with four electromagnetic actuators for the tracking of road measurements. IEEE Trans Ind Electron. 2011;58:5319-5323.

[7] Lorenz SJ, Sadeghi F, Trivedi HK, Kirsch MS. Investigation into rolling contact fatigue performance of aerospace bearing steels. Int J Fatigue. 2023;172:107646.

[8] Wang PH, Xiang QY, Królczyk G, Lu PM, Wang BH, Li ZX. Dynamic modeling of a hydraulic excavator stick by introducing multi-case synthesized load spectrum for bench fatigue test. Machines. 2022;10(9):741.

[9] Yin Y, Grondin GY, Obaia KH, Elwi AE. Fatigue life prediction of heavy mining equipment. Part 1: fatigue load assessment and crack growth rate tests. J Constr Steel Res. 2007;63:1494-1505.

[10] Bae H-R, Ando H, Nam S, Kim S, Ha C. Fatigue design load identification using engineering data analytics. J Mech Des. 2015;137:011001.

[11] Dressler K, Speckert M, Bitsch G. Virtual durability test rigs for automotive engineering. Veh Syst Dyn. 2009;47:387-401.

[12] Oppermann H, Bäcker M, Langthaler T. Computing drive signals at a virtual test rig: another step towards CAE-based durability analysis. Paper presented at: Meeting on Numerical Analysis and Simulation in Vehicle Engineering, Wurzburg, Germany, 1-2 October 2002. 2002.

[13] Kim HS, Yim HJ. Computational durability prediction of body structures in prototype vehicles. Int J Automot Technol. 2002;3:129-135.

[14] Sendur P, Ozcan U, Ozoguz B. A methodology to design multi-axis test rigs for vibration and durability testing using frequency response functions. Proc Meet Acoust. 2017;30:065001.

[15] Li D, Tian J, Shi S, Wang S, Deng J, He S. Lightweight design of commercial vehicle cab based on fatigue durability. Comput Model Eng Sci. 2023;136:421-445.

[16] Wang T, Wang L, Wang Y. Assessment of the locations of fatigue failure in a commercial vehicle cab using the virtual iteration method. Proc Inst Mech Eng D J Automob Eng. 2017;231:84-98.

[17] Bian X, Ma S, Yang J, Ge X. Fatigue analysis of truck cab based on virtual iteration method. Mach Des Res. 2017;33:170-173.

[18] Ge W, Gong C, Liu Z, Duan L, Huang H. Load extraction of leaf-spring bushing based on road load data and test-rig fatigue test. J Mech Strength. 2020;42:43-49.

[19] Daley S, Owens DH, Hatonen J. Application of optimal iterative learning control to the dynamic testing of mechanical structures. Proc Inst Mech Eng I J Syst Control Eng. 2007;221:211-222.

[20] Wang X, Cong D, Yang Z, Xu S, Han J. Modified quasi-Newton optimization algorithm-based iterative learning control for Multi-Axial road durability test rig. IEEE Access. 2019;7:31286-31296.

[21] Mueller T, Endisch C. Compensation techniques for iterative rig control in multi-axial durability testing. Paper presented at:21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, 6-9 September 2016. 2016.

[22] Wan Y, Song X, Yu L, Yuan Z. Load identification model and measurement method of loader working device. J Vib Meas Diagn. 2019;39:582-589.

[23] Kim S-H, Lee Y-S. Sun D-I, et al. Development of bulldozer sensor system for estimating the position of blade cutting edge. Automat Constr. 2019;106:102890.

[24] Czerlunczakiewicz E, Majerczak M, Bonato M. Variability of fatigue simulation predictions for automotive components. Paper presented at: 69th Annual Reliability and Maintainability Symposium (RAMS), Orlando, FL, USA, 23-26 January 2023. 2023.

[25] Liu JY, Hong JZ. Geometric stiffening effect on rigid-flexible coupling dynamics of an elastic beam. J Sound Vib. 2004;278:1147-1162.

[26] Heirman GHK, Desmet W. Interface reduction of flexible bodies for efficient modeling of body flexibility in multibody dynamics. Multibody Syst Dyn. 2010;24:219-234.

[27] Zhu X, Pan L, Sun Z, Wan Y, Huang Y, Choi J-H. Simulation tool for dozer data acquisition. Automat Constr. 2022;142:104522.

[28] Pan L, Zhu X, Li Y, Guan T. Lightweight design of an electric tricycle frame considering dynamic stress in driving conditions. Int J Automot Technol. 2021;22:1075-1085.

[29] Shin YJ, Jeong JS, Jun CW, Sohn JH. Interacting analysis between wheel and sand particles based on DEM and its validation with experiments. J Mech Sci Technol. 2020;34:4537-4544.

[30] Wasfy T, Jayakumar P. Next-generation NATO reference mobility model complex terramechanics-part 1: definition and literature review. J Terramech. 2021;96:45-57.

[31] Awuah E, Zhou J, Liang Z, et al. Parametric analysis and numerical optimisation of Jerusalem artichoke vibrating digging shovel using discrete element method. Soil Tillage Res. 2022;219:105344.

[32] Tekeste MZ, Way TR, Syed Z, Schafer RL. Modeling soil-bulldozer blade interaction using the discrete element method (DEM). J Terramech. 2020;88:41-52.

[33] Richter C, Roessler T, Otto H, Katterfeld A. Coupled discrete element and multibody simulation, part I: implementation, verification and validation. Powder Technol. 2021;379:494-504.

[34] Yan L, Cui G-H. Yuan H-Z, Zhou H-D. The kinematic solving of the 4-RPC redundantly actuated parallel mechanism based on RecurDyn. Modul Mach Tool Autom Manuf Tech. 2013:36-39.

[35] Delahay T, Palin-Luc T. Estimation of the fatigue strength distribution in high-cycle multiaxial fatigue taking into account the stress-strain gradient effect. Int J Fatigue. 2006;28:474-484.

[36] Baumgartner J, Waterkotte R, Hesseler J. Fatigue assessment of a welded automotive differential under multiaxial and variable amplitude loading. Int J Fatigue. 2021;149:106292.

[37] Zhang L, Jiang B, Zhang P, et al. Methods for fatigue-life estimation: a review of the current status and future trends. Nanotechnol Precis Eng. 2023;6:025001.

[38] Engineers, S.o.A. SAE J1099. Rev. 2002. Available online: https://www.docin.com/p-1750860016.html

[39] Li WB, Xu YR, Hu N, Deng MX. Impact damage detection in composites using a guided wave mixing technique. Meas Sci Technol. 2020;31:014001.

[40] Jiang C, Li W, Deng M, Ng C-T. Quasistatic pulse generation of ultrasonic guided waves propagation in composites. J Sound Vib. 2022;524:116764.

[41] Glinka G, Kam J. Rainflow counting algorithm for very long stress histories. Int J Fatigue. 1987;9:223-228.

[42] Meggiolaro MA, de Castro JTP. An improved multiaxial rainflow algorithm for non-proportional stress or strain histories—part II: the modified Wang-Brown method. Int J Fatigue. 2012;42:194-206.

[43] Zhao H, Wang G, Wang H, Bi Q, Li X. Fatigue life analysis of crawler chain link of excavator. Eng Fail Anal. 2017;79:737-748.

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2024 2024 The Author(s). International Journal of Mechanical System Dynamics published by John Wiley & Sons Australia, Ltd on behalf of Nanjing University of Science and Technology.