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:
Mar. 10, 2026

Filed:

Aug. 17, 2021
Applicant:

Commissariat a L'energie Atomique ET Aux Energies Alternatives, Paris, FR;

Inventors:

Clement Fisher, Orsay, FR;

Roberto Miorelli, Gif-sur-Yvette, FR;

Olivier Mesnil, Orsay, FR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G01N 21/88 (2006.01); G01N 23/02 (2006.01); G01N 25/72 (2006.01); G01N 27/90 (2021.01); G01N 29/44 (2006.01); G06N 3/0455 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G01N 21/88 (2013.01); G01N 23/02 (2013.01); G01N 25/72 (2013.01); G01N 27/9046 (2013.01); G01N 29/4481 (2013.01); G06N 3/0455 (2023.01); G01N 2201/126 (2013.01);
Abstract

A method is provided for characterizing a part. The method includes a) carrying out non-destructive measurements using a sensor, the sensor being placed on the part or facing the part; b) using the measurements as input data of a neural network; and c) depending on the value of each node of the output layer of the neural network, characterizing the part. The method further includes prior to steps b) and c): constructing a first database, on a first model part; employing a first neural network parametrized by a first training operation, using the first database; constructing a second database, containing experimental measurements of the physical quantity performed on the part to be characterized; a second training operation, using the second database, so as to parametrize a second neural network, using the parametrization of the first neural network. In step c), the neural network used is the second neural network.


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