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:
Oct. 21, 2025

Filed:

Aug. 21, 2023
Applicant:

Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US);

Inventors:

Vadim Pinskiy, Wayne, NJ (US);

Matthew C. Putman, Brooklyn, NY (US);

Damas Limoge, Brooklyn, NY (US);

Aswin Raghav Nirmaleswaran, Brooklyn, NY (US);

Assignee:

Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B29C 64/393 (2017.01); B22F 10/30 (2021.01); B22F 10/85 (2021.01); B22F 12/90 (2021.01); B29C 64/209 (2017.01); B33Y 10/00 (2015.01); B33Y 50/02 (2015.01); G06F 18/20 (2023.01); G06F 18/2411 (2023.01); G06N 3/04 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/98 (2022.01); B22F 10/12 (2021.01); B22F 10/18 (2021.01); B22F 10/25 (2021.01); B22F 10/28 (2021.01);
U.S. Cl.
CPC ...
B29C 64/393 (2017.08); B22F 10/30 (2021.01); B22F 10/85 (2021.01); B22F 12/90 (2021.01); B29C 64/209 (2017.08); B33Y 10/00 (2014.12); B33Y 50/02 (2014.12); G06F 18/2411 (2023.01); G06F 18/295 (2023.01); G06N 3/04 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/993 (2022.01); B22F 10/12 (2021.01); B22F 10/18 (2021.01); B22F 10/25 (2021.01); B22F 10/28 (2021.01);
Abstract

Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine that a non-recoverable failure is not present in the printed layer of the object; generate a cropped version of the low-resolution version of the captured image; apply a trained binary error classifier to the cropped version of the low-resolution version of the captured image; determine that an error is present in the printed layer of the object; apply a trained extrusion classifier to the captured image, wherein the trained extrusion classifier generates an extrusion quality score; and adjust a value of a parameter of the print head based on the extrusion quality score to print a subsequent layer of the printed object.


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