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Fatigue life life prediction prediction for spheroidal for - - PowerPoint PPT Presentation

Jun 16, 2023 25 likes •190 views

Fatigue life life prediction prediction for spheroidal for spheroidal graphite graphite Fatigue cast iron iron based based on on crack crack growth growth from from defects defects cast detected by by detected X- -ray ray

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Fatigue Fatigue life life prediction prediction for spheroidal for spheroidal graphite graphite cast cast iron iron based based on

  • n crack

crack growth growth from from defects defects detected detected by by X X-

  • ray

ray tomography tomography

Mehdi Shirani

  • Dept. of Engineering Design and Materials

NTNU, Trondheim

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SGCI EN SGCI EN-

  • GJS

GJS-

  • 400

400-

  • 18

18-

  • LT is used in

LT is used in wind wind-

  • turbine hubs (Vestas Castings, Kristiansand)

turbine hubs (Vestas Castings, Kristiansand)

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To establish methodology and improve data to optimize

large wind turbine components (by reducing the weight and increasing fatigue life)

FeVIND Main Goal FeVIND Main Goal

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Micro Micro-

  • graph of gas pores in ductile cast iron

graph of gas pores in ductile cast iron (Anders (Anders Bj Bjö örkblad rkblad, KTH, 2008) , KTH, 2008)

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FeVIND FeVIND activities activities

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Random Defect Single Defect Explicit FCG analysis da/dn = f(Ds, a; R), Weakest Link Local Stress Implicit FCG analysis S-N-curve (a > 1 mm), ‘crack initiation’ Probabilistic Deterministic Material properties Crack Growth

P P•

  • FAT

FAT Probabilistic Fatigue Assessment Tool Probabilistic Fatigue Assessment Tool developed at NTNU/IPM 2003 developed at NTNU/IPM 2003-

  • 2007

2007

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cross-sectional image of a Motorbike Casting

  • btained by

Computed Tomography

X X-

  • ray Computed Tomography

ray Computed Tomography

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Schematic Schematic of

  • f a CT system

a CT system

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By post processing CT results, it is By post processing CT results, it is possible to access size and location of possible to access size and location of defects defects

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Using previous data, P Using previous data, P •

  • FAT draws the defects

FAT draws the defects and calculates fatigue life and calculates fatigue life

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Is it possible to do CT for large parts such as wind turbine Hub? Due to the limited volume of steel that can be examined by

conventional inspection methods, the number and size of defects in a large volume have to be estimated by statistical analysis

What is the limitation of this method? What is the limitation of this method?

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P P •

  • FAT Random Defect Approach

FAT Random Defect Approach For every finite element of a component, random defects are For every finite element of a component, random defects are generated based on the underlying statistical distributions generated based on the underlying statistical distributions (number, location, size). (number, location, size).

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Example based on data obtained Example based on data obtained with a standard with a standard

  • ptical microscope for EN
  • ptical microscope for EN-
  • GJS

GJS-

  • 400

400-

  • 18

18-

  • LT

LT

Material Properties 0.05 number of defects per unit volume 0.05289 mm Defect size scale parameter 0.05973 mm Defect size location parameter Defect size shape parameter 8.56 (MPa, M) Threshold stress intensity range (R=0.1) 1.878E-13 C (R=0.1) Fatigue Crack Growth data 4.4 m Fatigue Crack Growth data 245 MPa Yield strength 403 MPa Tensile strength 150 MPa Fatigue limit (R=0.1)

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Stress range 243 Stress range 243 MPa MPa, R=0.1, Probability of failure is 80% , R=0.1, Probability of failure is 80%

  • **********************************************************************
  • PFAT - Main Result File
  • DATE: 27/ 1/ 2009
  • TIME: 22: 22: 7
  • **********************************************************************
  • FATIGUE ASSESSMENT MODULE: RANDOM DEFECT
  • **********************************************************************
  • Operating Finite Element File:C:\PFAT\FEA\SAMPLE12.vtf
  • Residual Finite Element File:None
  • Crack Growth Model: Short Crack Growth Model
  • Number of Analysed Components: 10
  • **********************************************************************
  • Parameters
  • Reference Load Amplitude: 1.21500000000
  • Reference Mean Load: 1.48500000000
  • Reference Residual Load: 0.00000000000
  • Fatigue Limit (amplitude) [MPa]: 150.000
  • Stress Ratio for the Fatigue Limit: 0.100000
  • Critical Crack Depth [mm]: 6.00000
  • Crack Growth Exponent: 4.40000
  • Crack Growth Constant [MPa,m]: 1.878000E-13
  • Stress Ratio for the Crack Growth Constant: 0.100000
  • Threshold Stress Intensity Factor Range [MPa,m]: 8.56000
  • Stress Ratio for the Threshold Stress Intensity Factor Range: 0.100000
  • Walker Exponent: 0.780000
  • Expected Number of Defects Per Unit Volume: 5.000000E-02
  • Defect Size Distribution: Generalised Extreme\Gumble
  • Defect Size Location Parameter [mm]: 5.973000E-02
  • Defect Size Scale Parameter [mm]: 5.289000E-02
  • **********************************************************************
  • col1: Component Number
  • col2: Fatigue Life
  • **********************************************************************
  • 1 473286
  • 2 212409
  • 3 301548
  • 4 787628
  • 5 No Critical Defects
  • 6 No Critical Defects
  • 7 537441
  • 8 552515
  • 9 563543
  • 10 434078
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Probability of failure vs. stress range obtained by Probability of failure vs. stress range obtained by P P •

  • FAT

FAT

Probability of failure vs stress range

20 40 60 80 100 120 210 220 230 240 250 260 stress range (MPa) %

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