nCode DesignLife - Fatigue life prediction and Test-CAE Correlation

Fatigue life prediction and Test-CAE Correlation

nCode DesignLife - Fatigue life prediction and Test-CAE Correlation

Fatigue life prediction and Test-CAE Correlation

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DesignLife for CAE-based fatigue analysis

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Durability prediction and fatigue analysis

Virtual Strain Gauge and Virtual Sensor
 
Enables correlation between test and finite element results. Gauges (single or rosette) or displacement sensors may be graphically positioned and oriented on finite models as a post-processing step. Time histories due to applied loads can be extracted for direct correlation with your measured strain or displacement data.

Crack Growth
Provides a complete fracture mechanics capability using industry standard methodologies for specified locations on an FE model. Built-in growth laws include NASGRO, Forman, Paris, Walker and more. Select from a pre-populated library of geometries or supply custom stress intensity factors.

Signal Processing 
nCode Fundamentals is included for basic data manipulation, analysis and visualization. Duty cycles can be defined by selecting from and building multiple cases. This feature makes it easy to create a composite duty cycle with repeats.

Materials Manager 
Enables materials data to be added, edited and plotted. A standard database with fatigue properties for many commonly used materials is included.

Custom Analysis
Enables Python or MATLAB scripts to be used to extend existing analysis capabilities - perfect for proprietary methods or research projects.

FE-Display 
Enables the graphical display of FE models with contours of stress results. Animating displacements from FE results or animation files display structural deformation under load.

Vibration Manager
Enables vibration specification data to be entered, edited, and viewed. A standard database containing over 100 vibration entries is included.

Processing Threads
Enables you to get finished results from raw inputs in faster. DesignLife can parallel process on machines with multiple processors, each Processing Thread license allowing another core to be utilized.

Stress-Life (SN) for high-cycle fatigue


The primary application of the Stress-Life (SN) method is high-cycle fatigue (long lives) where nominal stress controls the fatigue life. Includes the ability to interpolate multiple material data curves for factors such as mean stress or temperature. Further options are also provided to account for stress gradients and surface finishes. Python scripting is also available for defining custom fatigue methods and material models.

  • Material models: 
    • Standard SN
    • SN Mean multi-curve
    • SN R-ratio multi-curve
    • SN Haigh multi-curve
    • SN temperature multi-curve
    • Bastenaire SN
    • Custom SN using Python scripting
  • Stress combination methods or critical plane analysis
  • Back calculation to target life
  • Multiaxial assessment: 
    • Biaxial
    • 3D Multiaxial
    • Auto-correction
  • Mean stress corrections:
    • FKM Guidelines
    • Goodman
    • Gerber
    • Walker
    • Interpolate multiple curves
  • Notch correction:
    • Stress gradiant corrections
      • FKM Guidelines
      • User defined
    • Critical distance
Strain-Life (EN) for low-cycle fatigue

The Strain-Life method is applicable to a wide range of problems including low-cycle fatigue with the local elastic-plastic strain controls the fatigue life. The standard EN method uses the Coffin-Manson-Basquin formula, defining the relationship between strain amplitude εª and the number of cycles to failure Nf. Material models can also be defined using general look-up curves. This enables the ability to interpolate multiple material data curves for factors such as mean stress or temperature.

  • Material models: 
    • Standard EN
    • EN mean multi-curve
    • EN R-ratio multi-curve
    • EN temperature multi-curve
    • Gray Iron
  • Strain combination methods or critical plane analysis
  • Stress-strain tracking for accurate cycle positioning
  • Back calculation to target life
  • Multiaxial Damage models:
    • Wang Brown
    • Wang Brown with Mean
  • Mean stress corrections:
    • Morrow
    • Smith Watson Topper
    • Interpolate multiple curves
  • Plasticity corrections:
    • Neuber
    • Hoffman-Seeger
    • Seeger-Heuler
  • Multiaxial assessment:
    • Biaxial
    • 3D Multiaxial
    • Auto-correction
Predicting endurance limit

Dang Van is a multi-axial fatigue limit criterion and is a method of predicting the endurance limit under complex loading situations. The output from the analysis is expressed as a safety factor rather than fatigue life.

  • Uses specific material parameters calculated from tensile and torsion tests.
  • Manufacturing effects can be accounted for by using equivalent plastic strain in the unloaded component.
  • Can take into account the cut edge effect on thin sheet material
  • Includes new optimised and spot weld methods.
Calculating stress-based factors of safety

Safety Factor enables the calculation of stress based factors of safety. This method is widely used as a key design criteria for engine and powertrain components like crankshafts, camshafts and pistons.

  • Inputs are linear stress or strain for this SN based technique.
  • Material inputs are standard mean stress corrections or user-specified Haigh diagrams to assess durability.
  • Stresses from a complete finite element model are analyzed in a single analysis process.
Fatigue analysis of spot welds in thin sheets

The Spot Weld option enables the fatigue analysis of spot welds in thin sheets. The approach is based on the LBF method (see SAE paper 950711) and is well-suited to vehicle structure applications.
  • Spot welds are modelled by:
    • Stiff beam elements (e.g., NASTRAN CBAR), as supported by many leading FE pre-processors
    • Supports CWELD and ACM formulations using solid element representation
  • Cross sectional forces and moments are used to calculate structural stress around the edge of the weld
  • Life calculations are made around spot weld at multiple angle increments and the total life reported includes the worst case
  • Python scripting enables modelling of other joining methods such as rivets or bolts
Fatigue analysis of seam welds

DesignLife simplifies the process of setting up fatigue analysis of seam welds by intelligently identifying weld lines in an FE model. The Seam Weld option enables the fatigue analysis of seam welded joints including fillet, overlap, and laser welded joints. The method is based on the approach developed by Volvo (see also SAE paper 982311) and validated through years of use on vehicle chassis and body development projects.

  • Uses stresses either from FE models (shell or solid elements) or stresses from grid point forces or displacements at the weld
  • General material data for seam welds for both bending and tension conditions are supplied
  • Appropriate for weld toe, root and throat failures
  • Thick welds can be assessed using the stress integration method outlined in ASME Boiler & Pressure Vessel Code VIII (Division 2) standard
  • Automated method to identify the weld locations from the solid FE model
  • Corrections available for sheet thickness and mean stress effects.
  • Structural stress at the weld toe, the hot-spot stress can be estimated by the extrapolation of the surface stress at points near the weld
  • Supports BS7608 welding standard, together with required material curves
Improve accuracy of thick weld analysis

The methods used in the WholeLife option improves the accuracy of analysis of thick welds. It uses an integrated approach for modeling fatigue over the entire lifetime of a component - from the very early stages to final fracture - to give more accurate determination of weld lives particularly for complex geometries. The same structural stress technique used for seam welds is used in WholeLife to determine the structural bending and membrane stresses at the weld.

WholeLife uses the through thickness stress distribution for the geometry and can include the effect of a known residual stress profile. Although this is primarily a CAE based analysis, the same method may also be applied to measured stress data.
Frequency based fatigue analysis

The Vibration Fatigue option provides the capability to predict fatigue in the frequency domain and it is more realistic and efficient than time-domain analysis for applications with random loading such as wind and wave loads or where structures are excited by rotating machinery.
  • Simulates vibration shaker tests driven by random PSD, swept-sine, sine-dwell, or sine-on-random loading.
  • FE models are solved for frequency response or modal analysis. Vibration loading is defined in DesignLife and can include effect of multiple temperature and static offset load cases.
  • Complete duty cycles can combine different vibration loading types and then with time-domain loads for more complex loadings.
  • Multiple simultaneous frequency domain PSD loads can be applied including cross spectra to simulate real-world loading.
  • Frequency domain inputs can be quickly and directly generated from time series data.
  • Vibration fatigue can be used for stress-life, strain-life, seam weld, spot weld and short fibre composite analysis methods providing the most extensive frequency domain fatigue simulation capabilities commercially available.
High temperature fatigue and creep

The Thermo-Mechanical Fatigue (TMF) option provides solvers for high temperature fatigue and creep by using stress and temperature results from finite element simulations. Mechanical loads that vary at a different rate to the temperature variations can also be combined. Applications include components that are both mechanically and thermally loaded such as vehicle exhaust systems and manifolds.

High temperature fatigue methods:

  • Chaboche 
  • ChabocheTransient

Creep analysis methods:

  • Larson-Miller
  • Chaboche creep
Stress-life fatigue of anisotropic materials

The Short Fibre Composite option uses stress-life fatigue calculations for anisotropic materials such as glass fibre filled thermoplastics. The stress tensor for each layer and section integration point through the thickness is read by DesignLife from FE results. The material orientation tensor describing the “fibre share” at each calculation point is provided by mapping a manufacturing simulation to the finite element model. This orientation tensor can be read from the FE-results file or supplied from an ASCII file.

Short Fibre Composite module features: 

  • Simulate complex loading scenarios using any time domain method (static or modal superposition, duty cycles, etc.)
  • Simulate vibration tests driven by random (PSD), swept sine, sine dwell or sine-on-random loading
  • Predict damage and life per layer and integration point
  • Incorporate results of manufacturing simulation including fibre orientation tensors or residual stresses
  • Model local fatigue properties based on microstructure (orientation tensor) and stress state
  • Calculate fatigue based on principal stresses or critical plane — including stresses calculated from FE-Digimat and multiaxial stress states
  • Choice of fatigue property model - SN curve interpolation or interface to Digimat
  • Use of homogenized matrix or fibre stresses as well as typical composite ones
Calculate composite static failure criteria

The Composite Analysis option allows users to evaluate the strength of a structure against industry standard composite failure criteria. Rather than limiting this evaluation to a small number of load cases or steps, stresses can be assessed by using the chosen failure criteria throughout realistic duty cycles (quasi-static or dynamic). This allows critical locations, load combinations and associated design reserve factors to be readily identified. In addition, selected location loading paths may be visually compared with the material failure envelope.

The following methods can be used individually or combined to give the most conservative result:

  • Maximum stress
  • Maximum strain
  • Norris
  • Norris-McKinnon
  • Hoffman
  • Tsai-Hill
  • Tsai-Wu
  • Franklin-Marin
  • Hashin
  • Hashin-Rotem
  • Hashin-Sun
  • Christensen
  • Modified NU
  • User-defined custom methods via Python
Calculate loads from measured strains

The Strain Gauge Positioning option calculates the optimum position and number of gauges required to enable the subsequent reconstruction of applied load histories.

The Loads Reconstruction glyph uses the virtual strains created by unit loads along with the measured strain histories from gauges matching the virtual strain gauges to reconstruct the force histories that caused the measured strains.
Durability calculations on adhesive joints

nCode DesignLife uses a fracture mechanics-based method to assess which joints in the structure are most critically loaded. The Adhesive Bonds option enables durability calculations on adhesive joints in metallic structures. 

  • Adhesive bonds are modeled with beam elements and grid point forces are used to determine line forces and moments at the edge of the glued flange.
  • Approximate calculations of the strain energy release rate are made at the edge of the adhesive and, by comparison to the crack growth threshold, a safety factor is calculated.
  • The theoretical basis of the method was developed by the Volvo Group and the testing and software implementation was carried out as part of a collaborative research project with partners including Jaguar Land Rover, Coventry University and Warwick University.
Distributing jobs on remote or clustered computers

Distributed Processing enables a DesignLife analysis running in batch mode to be distributed across multiple computers or nodes of a computer cluster. 

  • Uses MPI standard that is common in high performance computing (HPC) environments so that even the largest of finite element simulations can be completed efficiently.
  • Enables you to rapidly solve jobs by using the combined processors of many machines.
  • Includes a batch interface program to simplify the running of distributed jobs.

Ready to achieve success through failure prediction?

Ready to achieve success through failure prediction?