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:
Dec. 06, 2022

Filed:

Apr. 03, 2019
Applicants:

Hyundai Motor Company, Seoul, KR;

Kia Motors Corporation, Seoul, KR;

Industry-university Cooperation Foundation Sogang University, Seoul, KR;

Inventors:

Yu Jin Bae, Seongnam-si, KR;

Jae Ho Kim, Seoul, KR;

Heung Jae Choi, Incheon, KR;

Dong Choul Kim, Seoul, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B62D 65/02 (2006.01); G01N 3/08 (2006.01); G01N 3/02 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G01N 3/02 (2013.01); B62D 65/02 (2013.01); G01N 3/08 (2013.01); G06N 3/08 (2013.01);
Abstract

A method of predicting joining strength of joined dissimilar materials, includes performing a joining strength test on a plurality of specimens of joined dissimilar materials each having different joining information, and acquiring force-displacement data on a basis of the joining information; constructing, in a prediction system, an artificial neural network model for predicting the force-displacement data and the joining strengths from the joining information; learning the artificial neural network model by inputting the force-displacement data to the prediction system, the force-displacement data obtained through the joining strength test; inputting joining information to be predicted to the prediction system by using a computer running a software for performing prediction for the joining strength and connected to a host computer of the prediction system through a network; and predicting, by the learned artificial neural network model, force-displacement value and joining strength.


Find Patent Forward Citations

Loading…