Method and apparatus for predicting the failure of a component

• US 20060206295A1
• Filed: 01/18/2006
• Published: 09/14/2006
• Est. Priority Date: 10/26/2000
• Status: Active Grant

• Current Assignee: Vextec Corporation
• Original Assignee: Vextec Corporation
• Inventors: Tryon, Robert G. III
• Granted Patent: US 7,480,601 B2
• Time in Patent Office: Days
• US Class Current: 703/6
• 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. 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 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 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.

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

3. The apparatus 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 apparatus of claim 1, wherein said simulating further comprises:

establishing an RVE life for each said RVE; and

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

5. The apparatus of claim 1, wherein each said at least one RVE microstructure-based failure model was ascertained following steps comprising:

identifying a material microstructure in said RVE;

characterizing how damage interacts with said 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 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 apparatus of claim 5 wherein said characterizing further comprises:

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

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

7. The apparatus 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 apparatus of claim 1, said simulating a component 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.

9. The apparatus of claim 8 wherein said probabilistic methods comprise Monte Carlo (MC) methods.

10. The apparatus of claim 1, said identifying further comprising obtaining a statistical distribution of said stresses at said significant nodes and said simulating a component further comprising:

determining an RVE life for each said RVE while using probabilistic methods to include a statistical distribution in said stresses.

11. The apparatus of claim 10 wherein said probabilistic methods comprise Monte Carlo methods.

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

13. The apparatus of claim 10, wherein said component has regions of similar geometric detail and said simulating further comprises adding a spatial correlation for said regions.

14. A method for determining the orientation factor for a grain slip system of a material, the method comprising:

obtaining equations that relate a stress direction to a material'"'"'s at least one potential slip system;

simulating a grain orientation of said material, said simulating comprising;

using probabilistic methods to generate a slip plane normal angle for each said at least one potential slip system;

inputting said normal angle into said equations to obtain a potential orientation factor for each said at least one potential slip system; and

selecting the least said potential orientation factor as a grain orientation factor for said grain orientation;

repeating said simulating for a defined number of grains and obtaining a plurality of grain orientation factors; and

creating a statistical distribution of said plurality of grain orientation factors to determine an orientation factor for said grain slip system.

15. The method of claim 14, said probabilistic methods comprising Monte Carlo methods.

16. The method of claim 14, said equations pertaining to titanium aluminide.

17. An apparatus for determining the orientation factor for a grain slip system of a material 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;

instructions for simulating a grain orientation of said material, said simulating comprising;

relating a stress direction to each of a material'"'"'s at least one potential slip system with equations;

using probabilistic methods to generate a slip plane normal angle for each said at least one potential slip system;

inputting said normal angle into said equations to obtain a potential orientation factor for each said at least one potential slip system; and

selecting a least said potential orientation factor as a grain orientation factor for said grain orientation;

instructions for repeating said simulating for a defined number of grains and obtaining a plurality of grain orientation factors; and

instructions for creating a statistical distribution of said plurality of grain orientation factors to determine an orientation factor for said grain slip system.

18. The apparatus of claim 17, said probabilistic methods comprising Monte Carlo methods.

19. The apparatus of claim 17, said equations pertaining to titanium aluminide.

20. 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, 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, wherein the one or more damage mechanisms include stress and fatigue; and

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

finding a sequence 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;

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.

21. 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;

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

22. The method of claim 21, wherein said performing said simulating a plurality of times comprises performing said simulating a first time using a first selected material microstructure of a plurality of available material microstructures associated with at least one component material, and further comprises performing said simulating a second time using a second different selected material microstructure of the plurality of available microstructures associated with the at least one component material.