The effectiveness of different wear indicators in quantifying wear on railway wheels of freight wagons

Abstract

Railway infrastructure relies on the dynamic interaction between wheels and rails; thus, assessing wheel wear is a critical aspect of maintenance and safety. This paper focuses on the wheel–rail wear indicator T-gamma (Tγ). Amidst its use, it becomes apparent that Tγ, while valuable, fails to provide a comprehensive reflection of the actual material removal and actual contact format, which means that using only Tγ as a target for optimization of profiles is not ideal. In this work, three different freight wagons are evaluated: a meter-gauge and a broad-gauge heavy haul vehicles from South American railways, and a standard-gauge freight vehicle operated in Europe, with different axle loads and dissimilar new wheel/rail profiles. These vehicles are subjected to comprehensive multibody simulations on various tracks. The simulations aimed to elucidate the intricate relationship between different wear indicators: Tγ, wear index, material removal, and maximum wear depth, under diverse curves, non-compensated lateral accelerations (A _nc), and speeds. Some findings showed a correlation of 0.96 between Tγ and wear depth and 0.82 between wear index and material removed for the outer wheel. From the results, the Tγ is better than the wear index to be used when analyzing wear depth while the wear index is more suited to foresee the material lost. The results also show the low influence of A _nc on wear index and Tγ. By considering these factors together, the study aims to improve the understanding of wheel–rail wear by selecting the best wear analysis approaches based on the effectiveness of each parameter.

References

[1.] Bernal E, Spiryagin M, Wu C . iNEW method for experimental-numerical locomotive studies focused on rail wear prediction. Mechan Syst Signal Process 2023, 186 109898

[2.] Lewis R, Dwyer-Joyce RS, Olofsson U . Mapping railway wheel material wear mechanisms and transitions. J Rail Rapid Transit 2010, 224 3 125-137

[3.] Bosso N, Magelli M, Zampieri N. Simulation of wheel and rail profile wear: a review of numerical models. Railw Eng Sci 2022, 30 4 403-436

[4.] Ye Y, Qi Y, Shi D . Rotary-scaling fine-tuning (RSFT) method for optimizing railway wheel profiles and its application to a locomotive. Railw Eng Sci 2020, 28 2 160-183

[5.] Hardwick C, Lewis R, Eadie D. Wheel and rail wear—understanding the effects of water and grease. Wear 2014, 314 1–2 198-204

[6.] Elkins JA, Eickhoff BM. Advances in non-linear wheel/rail force prediction methods and their validation. J Dyn Syst Meas Control 1982, 104 2 133-142

[7.] Sun Y, Spiryagin M, Cole C . Wheel–rail wear investigation on a heavy haul balloon loop track through simulations of slow speed wagon dynamics. Transport 2017, 33 3 843-852

[8.] Krishna VV, Hossein-Nia S, Casanueva C . Rail RCF damage quantification and comparison for different damage models. Railw Eng Sci 2022, 30 1 23-40

[9.] Santamaria J, Vadillo E, Oyarzabal O. Wheel–rail wear index prediction considering multiple contact patches. Wear 2009, 267 5–8 1100-1104

[10.] Harvey RF, McEwen IJ (1986) The relationship between wear number and wheel/rail wear in the laboratory and the field. British Rail Research Report TM-VDY-001

[11.] Ye Y, Hecht M (2022) Wear concentration index: an alternative to the target T-gamma in railway wheel profile optimization. In: Orlova A, Cole D (eds) Advances in dynamics of vehicles on roads and tracks II. IAVSD 2021. Lecture notes in mechanical engineering. Springer, Cham

[12.] Pacheco PADP, Endlich CS, Vieira KLS . Optimization of heavy haul railway wheel profile based on rolling contact fatigue and wear performance. Wear 2023, 522 204704

[13.] Ignesti M, Innocenti A, Marini L . Development of a wear model for the wheel profile optimisation on railway vehicles. Veh Syst Dyn 2013, 51 9 1363-1402

[14.] Montenegro PA, Calçada R. Wheel–rail contact model for railway vehicle–structure interaction applications: development and validation. Railw Eng Sci 2023, 31 3 181-206

[15.] Archard J. Contact and rubbing of flat surfaces. J Appl Phys 1953, 24 8 981-988

[16.] Du G, Han F, Fan X, Li Y . Dynamic simulation analysis of rail track and track structure based on SIMPACK and ABAQUS. Web of Conf 2021, 248 03007

[17.] Ayasse JB, Chollet H. Determination of the wheel rail contact patch in semi-Hertzian conditions. Veh Syst Dyn 2005, 43 3 161-172

[18.] SIMPACK, About rail–wheel Pairs. In simpack user assistance, dassault systemes simula corp., 2022.

[19.] Corrêa PHA, Ramos PG, Fernandes R . Effect of primary suspension and friction wedge maintenance. Wear 2023, 524–525 204748

[20.] Pacheco P, Reis T, Ramos P et al (2023) Wear and fatigue-oriented wheel profile optimized for heavy haul. VI Simpósio de Engenharia Ferroviária, Campinas

[21.] Pacheco P, Correa PHA, Ramos PG et al (2023) Effect of transition functions on the dynamic behavior of heavy-haul wagons. In: 20th International Wheelset Congress, Chicago

[22.] Pacheco P, Lopes M, Correa P et al (2023) Influence of primary suspension parameters on the wear behaviour of heavy-haul railway wheels using multibody simulation. In: Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering, Tenerife, pp 1–5

[23.] Ramos P, Correa P, Texeira L et al (2022) Dynamic effect of hollow-worn wheels for freight rail vehicles in a consist. In: Proceedings of the 5th International Conference on Railway Technology: Research, Development and Maintenance. Montpellier, civil-comp conferences, Vol 1, Paper 21.4

[24.] Bosso N, Magelli M, Zampieri N. Dynamical effects of the increase of the axle load on european freight railway vehicles. Appl Sci 2023, 13 3 1318

[25.] Lima EA, Baruffaldi LB, Manetti JLB . Effect of truck shear pads on the dynamic behaviour of heavy haul railway cars. Veh Syst Dyn 2022, 60 4 1188-1208

[26.] Roviraa A, Roda A, Marshall MB . Experimental and numerical modelling of wheel–rail contact and wear. Wear 2011, 271 5–6 911-924

[27.] Lewisand R, Dwyer-Joyce RS. Wear mechanisms and transitions in railway wheel steels. Proc Inst Mechan Eng Part J J Eng Tribol 2004, 218 6 467-478

[28.] Lewis R, Braghin F, Ward A et al (2003) Integrating dynamics and wear modelling to predict railway wheel profile evolution. In: 6th international conference on contact mechanics and wear of rail/wheel systems, Gothenburg.

[29.] Liu B, Bruni S, Lewis R (2022) Numerical calculation of wear in rolling contact based on the Archard equation: effect of contact parameters and consideration of uncertainties. Wear 490–491:204188

[30.] Jendel T. Prediction of wheel profile wear—comparisons with field measurements. Wear 2002, 253 1–2 89-99

[31.] Lewis R, Olofsson U. Mapping rail wear regimes and transitions. Wear 2004, 257 7–8 721-729

[32.] Bosso N, Magelli M, Zampieri N. Study on the influence of the modelling strategy in the calculation of the worn profile of railway wheels. WIT Trans Built Environ 2022, 213 65-76

[33.] Associação Brasileira de Normas Técnicas (2019) NBR 16810: Via férrea—Superelevação em curvas

[34.] Rete Ferroviaria Italiana (2016) Prefazione generale all’orario di servizio in uso sulla infrastruttura ferroviaria nazionale per i convogli di RFI. Specification 15/2016

[35.] European Committee for Standardization (2006) Railway applications–wheelsets and bogies–wheels–tread profile, EN 13715:2006

[36.] Association of American Railroads (2011) Wheels, carbon steel specification M-107/M-208

[37.] Kuka N, Verardi R, Ariaudo C . Impact of maintenance conditions of vehicle components on the vehicle–track interaction loads. Proc IMechE Part C: J Mechan Eng Sci 2018, 232 15 2626-2641

[38.] Benesty J, Chen J, Huang Y . Noise reduction in speech processing 2009 Berlin, Heidelberg Springer

[39.] Ratner B. The correlation coefficient: Its values range between +1/−1, or do they?. J Target Meas Anal Mark 2009, 17 139-142

[40.] Shevtsov I, Markine V, Esveld C. Optimal design of wheel profile for railway vehicles. Wear 2006, 258 7–8 1022-1030

Funding

Vale S.A.(03-P-07533/2019); Conselho Nacional de Desenvolvimento Científico e Tecnológico(315304/2018-9); CAPES(88887.892546/2023-00)