Method and apparatus for predicting the failure of a component

• US 8,027,820 B2
• Filed: 01/16/2009
• Issued: 09/27/2011
• Est. Priority Date: 10/26/2000
• Status: Active Grant

• Current Assignee: Vextec Corporation
• Original Assignee: Vextec Corporation
• Inventors: Tryon, Robert G. III
• Primary Examiner(s): Proctor; Jason
• Assistant Examiner(s): Guill; Russ
• Application Number:
  US12/355,704
• Publication Number:
  US 20090240646A1
• Time in Patent Office: 984 Days
• Field of Search: 703/13, 703/7
• US Class Current: 703/7
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 30/23 using finite element methods [FEM] or finite difference methods [FDM]

Abstract

The invention provides a method and apparatus for predicting the failure of a component using a probabilistic model of a material'"'"'s microstructural-based response to fatigue. The method predicts the component failure by a computer simulation of multiple incarnations of real material behavior, or virtual prototyping. The virtual prototyping simulates the effects of characteristics that include grain size, grain orientation, micro-applied stress and micro-yield strength that are difficult to simulate with real specimens. The invention provides an apparatus for predicting the response of a component to fatigue using the method.

Claims

1. A method for predicting the failure of a component, the method comprising:

obtaining a Finite Element Model (FEM) of a component;

analyzing said FEM to obtain stresses at nodes of said FEM;

identifying a subset of said nodes as significant nodes based on said stresses;

determining a Representative Volume Element (RVE) for at least one of said significant nodes;

developing an RVE microstructure-based failure model for at least one said RVE;

simulating a component life using at least one RVE microstructure-based failure model, said simulating producing a result related to said component life;

performing said simulating a plurality of times to produce results related to component life;

preparing statistics using said results; and

comparing said statistics to a probability of failure (POF) criteria to determine whether said performing predicted failure for said component.

2. The method of claim 1, wherein said failure is due to fatigue.

3. The method of claim 1, wherein each said RVE microstructure-based failure model comprises at least one random variable and wherein probabilistic methods are used to provide values for said at least one random variable.

4. The method of claim 1, wherein said simulating further comprises:

establishing an RVE life for each said RVE; and

using each said RVE life to produce the result related to said component life.

5. The method of claim 1, said developing comprising:

identifying a material microstructure in said RVE;

characterizing how damage interacts with said material microstructure to provide at least one damage mechanism; and

creating a failure model for said material microstructure based on said at least one damage mechanism, said creating comprising;

finding a sequence in which said at least one damage mechanism works to damage said material microstructure;

getting for each said at least one damage mechanism one of a group of models consisting of;

a crack nucleation model, a short crack growth model, and a long crack growth model; and

linking said models to produce said RVE microstructure-based failure model based on information from said identifying, characterizing, and finding.

6. The method of claim 5, said characterizing further comprising:

determining said material microstructure'"'"'s mechanical characteristics; and

determining said material microstructure'"'"'s bulk elastic material characteristics.

7. The method of claim 5, wherein said finding comprises:

determining how many of said at least one damage mechanism are crack nucleation mechanisms;

determining how many of said at least one damage mechanism are short crack growth mechanisms;

determining how many of said at least one damage mechanism are long crack growth mechanisms; and

developing a strategy for linking said crack nucleation, short crack growth, and long crack growth mechanisms.

8. The method of claim 5, wherein said RVE microstructure-based failure model comprises random variables and wherein probabilistic methods are used to provide values for said random variables.

9. The method of claim 5, wherein said getting comprises developing at least one of said group of models.

10. The method of claim 5, wherein said getting further comprises:

identifying variables that are important in the description of each said at least one damage mechanism;

relating said variables that are important to one of a group of damage mechanisms consisting of;

a crack nucleation mechanism, a short crack growth mechanism, and a long crack growth mechanism to form, respectively, one of a group of models consisting of;

a crack nucleation model, a short crack growth model, and a long crack growth model; and

defining output from said one of a group of models.

11. The method of claim 1, said simulating further comprising:

determining an RVE life for each said RVE, said determining an RVE life comprising;

evaluating a statistically determined number of nucleation sites within said RVE utilizing probabilistic methods.

12. The method of claim 1, said identifying further comprising:

obtaining a statistical distribution of said stresses at said significant nodes and said simulating a component life further comprising;

establishing a RVE life for each said RVE using probabilistic methods and said statistical distribution.

13. The method of claim 1, wherein said component has regions of similar geometric detail and said simulating further comprises adding a spatial correlation for said regions.

14. An apparatus for predicting the failure of a component comprising:

a central processing unit (CPU);

an output device for displaying simulated fatigue results;

an input device for receiving input; and

a memory comprising;

instructions for receiving input comprising;

a component'"'"'s material characteristics, a Finite Element Model (FEM) of said component, and at least one Representative Volume Element (RVE) microstructure-based failure model;

instructions for predicting failure of said component comprising;

analyzing said FEM to obtain stresses at nodes of said FEM;

identifying a subset of said nodes as significant nodes based on said stresses;

determining an RVE for at least one of said significant nodes;

simulating a component life using at least one RVE microstructure-based failure model, said simulating producing a result related to component life;

performing said simulating a plurality of times to produce results related to component life;

preparing statistics using said results; and

comparing said statistics to a probability of failure (POF) criteria to determine whether said performing predicted failure for said component; and

instructions for displaying a result from said predicting.

15. The apparatus of claim 14, wherein said failure is due to fatigue.

16. The apparatus of claim 14, wherein each said RVE microstructure-based failure model comprises at least one random variable and wherein probabilistic methods are used to provide values for said at least one random variable.

17. The apparatus of claim 14, wherein said simulating further comprises instructions for:

establishing an RVE life for each said RVE; and

using each said RVE life to produce a result related to said component life.

18. The apparatus of claim 14, said simulating further comprising instructions for:

determining an RVE life for each said RVE, said determining an RVE life comprising;

evaluating a statistically determined number of nucleation sites within said RVE utilizing probabilistic methods.

19. The apparatus of claim 14, said identifying further comprising instructions for:

obtaining a statistical distribution of said stresses at said significant nodes and said simulating a component life further comprising;

establishing a RVE life for each said RVE using probabilistic methods and said statistical distribution.

20. The apparatus of claim 14, wherein said component has regions of similar geometric detail and said simulating further comprises instructions for adding a spatial correlation for said regions.

21. The apparatus of claim 14, wherein said RVE microstructure-based failure model comprises:

at least one damage mechanism;

and wherein said at least one damage mechanism includes one of a group of models consisting of;

a crack nucleation model, a short crack growth model, and a long crack growth model.