The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 01, 2022

Filed:

Aug. 17, 2018
Applicant:

United Technologies Corporation, Farmington, CT (US);

Inventors:

Nagendra Somanath, South Windsor, CT (US);

Ryan B. Noraas, Hartford, CT (US);

Michael J. Giering, Bolton, CT (US);

Assignee:

Raytheon Technologies Corporation, Farmington, CT (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); B64F 5/10 (2017.01); G05B 19/4097 (2006.01); G06K 9/62 (2022.01); G06N 3/08 (2006.01); G06T 3/40 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
B64F 5/10 (2017.01); G05B 19/4097 (2013.01); G06K 9/6257 (2013.01); G06N 3/08 (2013.01); G06T 3/40 (2013.01); G06T 7/0004 (2013.01); G05B 2219/45071 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30136 (2013.01);
Abstract

A method for designing a material for an aircraft component includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy to the neural network. Each of the images in the set of images has varied constituent compositions. The method further includes providing the neural network with a set of determined material properties corresponding to each image, associating the microstructural features of each image with the set of empirically determined data corresponding to the image, and determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.


Find Patent Forward Citations

Loading…