Methods and apparatus for predicting the failure of a component, and for determining a grain orientation factor for a material
First Claim
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1. An apparatus comprising:
- a central processing unit (CPU)an output device for displaying simulated fatigue results;
an input device for receiving input;
and a memory,the CPU, memory, input device and output device being connected to one another via a bus, and the memory comprising;
instructions for predicting a probability of failure of a component, the instructions comprising;
obtaining a Finite Element Model (FEM) of the component;
analyzing said FEM to obtain stresses at nodes of said FEM;
determining a Representative Volume Element (RVE) for at least one of said nodes;
building a microstructure-based failure model for at least one said RVE and including the microstructure-based failure model in the RVE;
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;
using the results to predict a probability of failure for said component; and
instructions for displaying a result from said predicting.
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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.
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Citations
28 Claims
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1. An apparatus comprising:
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a central processing unit (CPU) an output device for displaying simulated fatigue results; an input device for receiving input; and a memory, the CPU, memory, input device and output device being connected to one another via a bus, and the memory comprising; instructions for predicting a probability of failure of a component, the instructions comprising; obtaining a Finite Element Model (FEM) of the component; analyzing said FEM to obtain stresses at nodes of said FEM; determining a Representative Volume Element (RVE) for at least one of said nodes; building a microstructure-based failure model for at least one said RVE and including the microstructure-based failure model in the RVE; 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; using the results to predict a probability of failure for said component; and instructions for displaying a result from said predicting. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A method, performed on a computer, for determining a grain orientation factor for a material, the method comprising:
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obtaining one or more equations that relate a stress direction to a slip direction for one or more respective potential slip systems of the material; simulating a grain orientation of said material, said simulating comprising, for each grain of a defined number of grains; using probabilistic methods to generate a slip plane normal angle for each of the potential slip systems in the grain; inputting said normal angles into said equations to obtain a potential orientation factor for each of the potential slip systems; and selecting the minimum potential orientation factor as a grain orientation factor for said grain; repeating said simulating for the defined number of grains thereby obtaining a plurality of grain orientation factors; creating a statistical distribution of said plurality of grain orientation factors to determine the grain orientation factor for said material and displaying at least the grain orientation factor for the material on an output device of the computer. - View Dependent Claims (20, 21)
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22. An apparatus for determining a grain orientation factor for a material, the apparatus comprising:
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a central processing unit (CPU);
an output device for displaying at least the grain orientation factor;an input device for receiving input; and a memory, the CPU, memory, input device and output device being connected to one another via a bus, and the memory comprising; instructions for receiving input; instructions for obtaining one or more equations that relate a stress direction to one or more respective potential slip systems of the material; instructions for simulating the grain orientation factor of said material, said simulating comprising, for each grain of a defined number of grains; using probabilistic methods to generate a slip plane normal angle for each of the potential slip systems in the grain; inputting said normal angles into said equations to obtain a potential orientation factor for each of the potential slip systems; and selecting a minimum potential orientation factor as a grain orientation factor for said grain; instructions for repeating said simulating for the defined number of grains thereby obtaining a plurality of grain orientation factors; instructions for creating a statistical distribution of said plurality of grain orientation factors to determine the grain orientation factor for said material. - View Dependent Claims (23, 24)
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25. A method, performed on a computer, for predicting the failure of a component made from a material, the method comprising:
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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 at least one damage mechanism includes 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 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; andlinking 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; comparing said statistics to a probability of failure (POF) criterion to determine whether said performing predicted failure for said component and displaying an indication of whether the failure was predicted on an output device of the computer. - View Dependent Claims (26)
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27. A method, performed on a computer, for predicting the failure of a component, the method comprising:
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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) criterion to determine whether said performing predicted failure for said component; and displaying an indication of whether the failure was predicted on an output device of the computer. - View Dependent Claims (28)
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Specification