UNIVERSAL AND AUTOMATED MONTE CARLO METHOD CODE FOR UNCERTAINTY PROPAGATION IN METROLOGY DATABASES

• US 20170371841A1
• Filed: 06/27/2016
• Published: 12/28/2017
• Est. Priority Date: 06/27/2016
• Status: Active Grant

• Current Assignee: U.S.A. as represented by the Administrator of the National Aeronautics and Space Administration
• Original Assignee: The United States of America As Represented By The Secretary of Agriculture
• Inventors: HAYDEN, JOSEPH, Khreishi, Manal A., Hadjimichael, Theodore A., Ohl, Raymond J.
• Granted Patent: US 9,990,335 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 17/145 Square transforms, e.g. Hadamard, Walsh, Haar, Hough, Slant transforms
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 2207/58 Indexing scheme relating to groups G06F7/58 - G06F7/588
• CPC Class Codes: G06F 7/58 Random or pseudo-random number generators

Abstract

A method of providing transformed target points for integrating a component into an assembly includes collecting a set of component target points, collecting a set of assembly target points, identifying target points common to the set of component target points and the set of assembly target points; performing a specified number of Monte Carlo transformations of selected ones of the common target points to yield a set of transformed target points and vectors and an associated uncertainty value for each transformed target point and vector, and using certain ones of the transformed target points for integrating the component into the assembly based on the associated uncertainty value for each of the transformed target points.

Claims

1. A method of providing transformed target points for integrating a component into an assembly comprising:

collecting a set of component target points and vectors;

collecting a set of assembly target points and vectors;

identifying target points common to the set of component target points and the set of assembly target points;

performing a specified number of Monte Carlo transformations of selected ones of the common target points to yield a set of transformed target points and vectors and an associated uncertainty value for each transformed target point and vector; and

using certain ones of the transformed target points for integrating the component into the assembly based on the associated uncertainty value for each of the transformed target points.

2. The method of claim 1, comprising using a networked set of metrology instruments to collect the component target point and assembly target point sets.

3. The method of claim 1, comprising determining the selected ones of the common target points based on residuals of a best fit transformation between the common target points.

4. The method of claim 1, wherein performing the specified number of Monte Carlo transformations includes calculating N-random 95% confidence interval (CI) uncertainties for each of the selected common target points.

5. The method of claim 4, wherein the N-random 95% CI uncertainties are calculated for each selected common target using a random number generator that draws from a Student'"'"'s-T probability distribution function.

6. The method of claim 5, wherein the Students-T distribution function is a function of degrees-of-freedom of each for each selected common target.

7. The method of claim 5, wherein the Student'"'"'s-T probability distribution function is scaled by the X, Y, and Z 95% CI uncertainties specified for each selected common target point.

8. An apparatus for transforming target points for integrating a component into an assembly comprising:

a metrology system for measuring a set of component target points and a set of assembly target points; and

a computer operable to;

identify target points common to the set of component target points and the set of assembly target points;

perform a specified number of Monte Carlo transformations of selected ones of the common target points to yield a set of transformed target points and an associated uncertainty value for each transformed target point; and

using certain ones of the transformed target points for integrating the component into the assembly based on the associated uncertainty value for each of the transformed target points.

9. The apparatus of claim 8, wherein the metrology system includes a networked set of metrology instruments for measuring the component target point and assembly target point sets.

10. The apparatus of claim 8, wherein the computer is operable to determine the selected ones of the common target points based on residuals of a best fit transformation between the common target points.

11. The apparatus of claim 8, wherein the computer is operable to perform the specified number of Monte Carlo transformations by calculating N-random 95% CI uncertainties for each of the selected common target points.

12. The apparatus of claim 8, wherein the computer is operable to calculate the N-random 95% confidence interval (CI) uncertainties for each selected common target using a random number generator that draws from a Student'"'"'s-T probability distribution function.

13. The apparatus of claim 12, wherein the Students-T distribution function is a function of degrees-of-freedom and a probability of each selected common target.

14. The apparatus of claim 12, wherein the computer is operable to scale the Student'"'"'s-T probability distribution function by the X, Y, and Z 95% CI uncertainties specified for each selected common target point.