Steel Databases: False Friends of Fatigue Simulation Uncertainty in steel properties, uncertainty in simulation results 15th Nov 2007 Bilbao, Spain
Material properties: the first brick of fatigue simulation The reliability of results in fatigue simulation depends on the reliability of material properties Any disagreement between material properties on databases and real properties on component leads to big errors in fatigue life predictions
Steel: a well-known material Steel is a traditional structural material, used for centuries Steel is the material with the best ratio between strength, weight and cost More than a 55% of passenger car weight is steel Among typical materials, it presents fatigue limit and properties data are available for most common grades 60 40 20 0 Acero Steel Fundición Cast Iron Aluminio Aluminium Plástico Plastic Elastómeros, Glass, Elastics Vidrio Cobre, Copper, Zinc, etc Fluidos Fluids
However It is a traditional material, but hundreds of new high strength steel grades have been developed in recent years Its fatigue behaviour is superior to other structural materials, but only a few part of steel grades are well characterised Available information is usually incomplete and difficult to compare
How steel is made Steelmaking routes affect to obtainable chemical composition in terms of tramp elements (Cu, Sn, As, P, S), degassing (N, O, H) and also inclusion cleanliness Iron Ore Blast Furnace AOF RH Continuous Casting Scrap EAF LF VD Ingot Casting
Who does what and how? Metallurgical practices are know-how of each steelmaker and final product quality depends strongly of who does what. 100 90 80 70 60 50 40 30 20 10 0 Failure Probability (%) B10: >50.000 cycles 720 +/- 630 MPa Automotive component Component life (cycles) 0 50000 100000 150000 Differences in lifetime can reach 70% from the best to the worst steel producer applying similar steelmaking routes
Steel processing modifies the final product Microalloying and thermomechanical history strongly influences on metallurgical and mechanical properties of steels Microprecipitates state and distribution can be modified and leads to grain growth and completely different fatigue behaviour Forging Annealing Machining Carburising Stress relieving Fine Grain Duplex Grain Coarse Grain
Small additions can change fatigue behaviour Several methods to enhance machinability and to deteriorate fatigue, but how much? 42CrMo4 SC Superclean Process 42CrMo4 D -S 42CrMo4 Pb +Pb, Bi +Ca 42CrMo4 Std +S 42CrMo4 Ca 42CrMo4 S All of them seem the same grade, but are completely different in fatigue 42CrMo4 GR +Se, Te +S 42CrMo4 R
Strength is a good design parameter Same tensile strength, but different microstructures and notably different fatigue lifetime Although tensile strength is an essential design tool, microstructure, inclusion cleanliness, toughness and so on should be also considered maximum shear stress (MPa) 360,0 340,0 320,0 300,0 280,0 260,0 240,0 Torsion Fatigue pearlite medium carbon tempered martensite low carbon tempered martensite 220,0 1,0E+04 1,0E+05 No Cycles 1,0E+06 1,0E+07
Fatigue limit? Depending on the conditions Notch sensitiveness in hard steels leads them to failure at very high fatigue lives (gigacyclic domain) Different steels have completely different fatigue behaviour
Oxides, sulphides, nitrides Inclusions are detrimental, but not all at the same level: size, shape, thermal dilatation coefficient and hardness influence strongly on fatigue behaviour
More size, more defects Same steel grade, same industrial heat, same rolling reduction ratio but different fatigue results depending on the sample size Specimen size is important, but specimen dimensions are not pointed out in most fatigue data records Specimen diameter (mm) 25,0 9,5 5,0 3,0 Ä Fatigue Limit (%) -22% 0% +26% +44%
Dispersion is a fact Dispersion of fatigue results is relatively high, particularly when inclusions are the typical cause of failure Fatigue data records of steels usually do not include number of tested specimens in each level or even confidence intervals for the fatigue limit or the lifetime at different stress loads 99,9 99 90 70 50 30 20 10 5 0,5 1 Wöhler curve 0,1 1000 10000 100000 1,E6 1,E7 1,E8 1,E9 HRC45-4,8E6 Weibull analysis Confidence levels
Multiaxial loads increase uncertainty on fatigue results Most fatigue data are obtained testing in rolling direction Uniaxial fatigue testing is much easier and simpler Often there is no enough material to test in transversal direction (i.e, wire rod) Rolling reduction and soft inclusions like MnS increase anisotropy and reduce shear properties Shear fatigue limit usually is not available and it is supposed as 0,5-0,6ó f Dang Van diagram
Loads in service coincide sometimes Typical standardised fatigue data records come from rotating bending fatigue tests, the simplest and cheapest But, in automotive components, service loads can be a combination of tensile-compression, torsion, pure bending, rolling contact stresses under or not corrosion conditions or thermal loads Fatigue behaviour changes depending on the steel grade, microstructure and cleanliness and the external stresses
Therefore They are steel databases but a lot of new steel grades currently used are not included Steelmaking routes have a notably influence on obtainable chemical compositions, particularly on tramp elements and gases Metallurgical practices and know-how in steel manufacturing can improve or reduce the fatigue response Thermomechanical processes modify the kinetics of precipitates and grain size control in subsequent thermal treatments Standard grades have several variants with enhanced machinability and completely different fatigue behaviour
and Tensile or yield strength cannot be the only design parameter, as fatigue response is clearly affected by other second order factors Not all the steels have similar fatigue behaviour, particularly high strength steels over 1500 MPa can fail due to inclusions at very high lifetime Spatial distribution, size, shape and type of inclusions affect deeply to the nucleation time and growth speed of fatigue cracks Specimen size or scale dependence should be specified when fatigue results are shown, especially if final component is much bigger than tested specimens
Moreover Very few steels have a complete Wöhler curve with enough specimens tested in each load level to obtain an accurate statistical average value and suitable confidence levels Multiaxial loading is not well characterised for most of current steel grades As steel is not an isotropic material, when combined loads are acting, extrapolation of uniaxial fatigue testing can be tricky
How the problem is solved? Typically, automotive system manufacturers test themselves their components and validate their own suppliers (steelmakers, forgers and so on) These means a great effort to perform the fatigue tests in-doors and great difficulties to introduce new developments This information is key for the companies and so it is not available for general purposes CAE designers must work with obsolete steel databases and apply really wide security factors New steel grades are not always available to be used in general design
How it should be solved? Encouraging automakers, OEMs, transformers and steelmakers to build an International Steel DataBase wider, wellcharacterised and including the different effects of metallurgical variants on fatigue behaviour Encouraging research institutes and universities to collect all the available information on fatigue, classify and store it and put into the web for general use
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