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:
Feb. 22, 2022

Filed:

Feb. 27, 2019
Applicant:

The Boeing Company, Chicago, IL (US);

Inventors:

Phillip John Crothers, Hampton East, AU;

Carla Elizabeth Reynolds, Shawnee, KS (US);

Alexander Rubin, St. Louis, MO (US);

Samuel J. Tucker, St. Louis, MO (US);

Gregg Robert Bogucki, Saint Charles, MO (US);

Joshua David Kalin, Huntsville, AL (US);

Assignee:

The Boeing Company, Chicago, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 19/4099 (2006.01); B33Y 50/02 (2015.01); G06F 30/15 (2020.01); B29C 70/38 (2006.01); G05B 13/02 (2006.01); G06F 119/18 (2020.01);
U.S. Cl.
CPC ...
G05B 19/4099 (2013.01); B29C 70/382 (2013.01); B33Y 50/02 (2014.12); G05B 13/0265 (2013.01); G06F 30/15 (2020.01); G05B 2219/33034 (2013.01); G05B 2219/35134 (2013.01); G05B 2219/49007 (2013.01); G06F 2119/18 (2020.01);
Abstract

A system to aid in design for manufacturing an object includes a processor and a memory configured to store instructions. The processor is configured to receive first data representing a design of the object to be manufactured and second data representing a machine-learning model. The processor is configured to execute the instructions to generate third data using the first data and the second data. The third data indicates at least one of a modification to the design of the object or process conditions for production of the object. The processor is configured to send the design of the object, the process conditions, or both, to a manufacturing tool to enable production of the object. The machine-learning model is representative of production data and based at least partially on one or more of: object features, process parameters, environmental factors, and quality data.


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