Enhanced component dimensioning

Patent metadata

Field	Value
Publication number	US-11449651-B2
Application number	US-201916674645-A
Country	US
Kind code	B2
Filing date	Nov 5, 2019
Priority date	Nov 5, 2019
Publication date	Sep 20, 2022
Grant date	Sep 20, 2022

Abstract

A computer includes a processor and a memory storing instructions executable by the processor to generate an operation parameter histogram for each of a plurality of vehicle operation parameters, operate a test vehicle according to the operation parameter histograms, input each operation parameter histogram and a plurality of test vehicle environment data to a machine learning program, output from the machine learning program a classification for each operation parameter histogram from the machine learning program, input the classifications to a predictive damage model of a three-dimensional schematic of a vehicle component, and adjust one of a size or a shape of the three-dimensional schematic based the output of the predictive damage model. The operation parameter histograms are based on collected data about operation of a plurality of vehicles. Each operation parameter histogram includes an array of elements. Each element is a number of data points for one of the operation parameters that are within a specified range. Each classification represents a type of environment and a type of test vehicle use.

First claim

What is claimed is:

1. A system, comprising a computer including a processor and a memory, the memory storing instructions executable by the processor to:
generate an operation parameter histogram for each of a plurality of vehicle operation parameters based on collected data about operation of a plurality of vehicles, each operation parameter histogram including an array of elements, each element being a number of data points for one of the operation parameters that are within a specified range;
operate a test vehicle according to the operation parameter histograms;
input each operation parameter histogram and a plurality of test vehicle environment data to a machine learning program;
output from the machine learning program a classification for each operation parameter histogram from the machine learning program, each classification representing a type of environment and a type of test vehicle use;
input the classifications to a predictive damage model of a three-dimensional schematic of a vehicle component; and
adjust one of a size or a shape of the three-dimensional schematic based the output of the predictive damage model.

2. The system of claim 1 , wherein the instructions further include instructions to, upon adjusting one of the size or the shape of the three-dimensional schematic, input the classifications to a predictive damage model of the adjusted three-dimensional schematic and adjust one of a size or a shape of the adjusted three-dimensional schematic based on the output of the predictive damage model.

3. The system of claim 2 , wherein the instructions further include instructions to input the classifications and successive adjusted three-dimensional schematics to successive predictive damage models until an optimization criterion for at least one of the size or the shape of the three-dimensional schematic is satisfied.

4. The system of claim 1 , wherein the instructions further include instructions to adjust a material thickness of the three-dimensional schematic based on the output of the predictive damage model.

5. The system of claim 1 , wherein the output of the predictive damage model includes a fatigue limit specifying a stress on a virtual component modeled according to the three-dimensional schematic that generates a strain exceeding a material yield strength.

6. The system of claim 5 , wherein the fatigue limit is a number of revolutions of the virtual component rotating at a specified torque.

7. The system of claim 1 , wherein the instructions further include instructions to input performance data from a vehicle dynamics model to a predictive damage model for a virtual component constructed according to the three-dimensional schematic.

8. The system of claim 7 , wherein the vehicle dynamics model includes a model of a plurality of road segments and the performance data for the virtual component includes performance data for each road segment.

9. The system of claim 7 , wherein the vehicle dynamics model includes a model of a plurality of road conditions, and the performance data includes data of the virtual component operating in the plurality of road conditions.

10. The system of claim 9 , wherein each road condition is associated with one of the classifications.

11. The system of claim 1 , wherein the type of test vehicle use includes at least one of a specified speed limit, a stop light, or a traffic intersection.

12. The system of claim 1 , wherein the operation parameters include at least one of a vehicle speed, a steering torque, a braking torque, a fuel flow rate, or a transmission speed.

13. The system of claim 1 , wherein the instructions further include instructions to adjust the size of the three-dimensional schematic to reduce a weight of a vehicle component constructed according to the three-dimensional schematic.

14. The system of claim 1 , wherein the type of environment includes at least one of a rural environment, an urban environment, and a highway environment.

15. A method, comprising:
generating an operation parameter histogram for each of a plurality of vehicle operation parameters based on collected data about operation of a plurality of vehicles, each operation parameter histogram including an array of elements, each element being a number of data points for one of the operation parameters that are within a specified range;
operating a test vehicle according to the operation parameter histograms;
inputting each operation parameter histogram and a plurality of test vehicle environment data to a machine learning program;
outputting from the machine learning program a classification for each operation parameter histogram from the machine learning program, each classification representing a type of environment and a type of test vehicle use;
inputting the classifications to a predictive damage model of a three-dimensional schematic of a vehicle component; and
adjusting one of a size or a shape of the three-dimensional schematic based the output of the predictive damage model.

16. The method of claim 15 , further comprising, upon adjusting one of the size or the shape of the three-dimensional schematic, inputting the classifications to a predictive damage model of the adjusted three-dimensional schematic and adjusting one of a size or a shape of the adjusted three-dimensional schematic based on the output of the predictive damage model.

17. The method of claim 15 , further comprising inputting performance data from a vehicle dynamics model to a predictive damage model for a virtual component constructed according to the three-dimensional schematic.

18. A system, comprising:
a test vehicle including a plurality of vehicle sensors;
means for generating an operation parameter histogram for each of a plurality of vehicle operation parameters based on collected data about operation of a plurality of vehicles, each operation parameter histogram including an array of elements, each element being a number of data points for one of the operation parameters that are within a specified range;
means for operating a test vehicle according to the operation parameter histograms;
means for inputting each operation parameter histogram and a plurality of test vehicle environment data to a machine learning program;
means for outputting from the machine learning program a classification for each operation parameter histogram from the machine learning program, each classification representing a type of environment and a type of test vehicle use;
means for inputting the classifications to a predictive damage model of a three-dimensional schematic of a vehicle component; and
means for adjusting one of a size or a shape of the three-dimensional schematic based on the output of the predictive damage model.

19. The system of claim 18 , further comprising means for inputting the classifications to a predictive damage model of the adjusted three-dimensional schematic and adjusting one of a size or a shape of the adjusted three-dimensional schematic based on the output of the predictive damage model upon adjusting one of the size or the shape of the three-dimensional schematic.

20. The system of claim 18 , further comprising means for inputting performance data from a vehicle dynamics model to a predictive damage model for a virtual component constructed according to the three-dimensional schematic.

Assignees

• Ford Global Tech Llc

Inventors

• Ostrowski David
• Jones Christopher Leon
• Povirk Jacob Martin
• Posa Andrew Christipher
• Krysztof Charles
• Stranges Antonio

Classifications

• G07C5/085

  using electronic data carriers · CPC title

• G06F2119/02

  Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA] · CPC title

• G06N20/00

  Machine learning · CPC title

• G05D1/0088

  characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00) · CPC title

• G06F30/15 Primary

  Vehicle, aircraft or watercraft design · CPC title