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. 12, 2024

Filed:

Oct. 28, 2020
Applicant:

Nantong University, Nantong, CN;

Inventors:

Liang Hua, Nantong, CN;

Ling Jiang, Nantong, CN;

Juping Gu, Nantong, CN;

Cheng Lu, Nantong, CN;

Kun Zhang, Nantong, CN;

Kecai Cao, Nantong, CN;

Liangliang Shang, Nantong, CN;

Qi Zhang, Nantong, CN;

Shenfeng Wang, Nantong, CN;

Yuxuan Ge, Nantong, CN;

Zixi Ling, Nantong, CN;

Jiawei Miao, Nantong, CN;

Assignee:

NANTONG UNIVERSITY, Nantong, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01N 33/207 (2019.01); B23K 37/00 (2006.01); G01N 29/04 (2006.01); G01N 29/46 (2006.01); G06N 3/08 (2023.01); G06T 7/11 (2017.01); H04R 1/08 (2006.01); H04R 3/04 (2006.01); B23K 31/00 (2006.01); B23K 31/12 (2006.01); G01N 29/34 (2006.01); G01N 29/44 (2006.01);
U.S. Cl.
CPC ...
G01N 29/045 (2013.01); B23K 37/00 (2013.01); G01N 29/46 (2013.01); G01N 33/207 (2019.01); G06N 3/08 (2013.01); H04R 3/04 (2013.01); B23K 31/006 (2013.01); B23K 31/125 (2013.01); G01N 29/348 (2013.01); G01N 29/4481 (2013.01); G01N 2291/0234 (2013.01); G01N 2291/0289 (2013.01); G01N 2291/267 (2013.01); G01N 2291/2675 (2013.01); G06T 7/11 (2017.01); G06T 2207/20084 (2013.01); H04R 1/08 (2013.01);
Abstract

A smart acoustic information recognition-based welded weld impact quality determination method and system, comprising: controlling a tip of an ultrasonic impact gun () to perform impact treatment on a welded weld with different treatment pressures, treatment speeds, treatment angles and impact frequencies, obtaining acoustic signals during the impact treatment, calculating feature values of the acoustic signals, and constructing an acoustic signal sample set including various stress conditions; marking the acoustic signal sample set according to impact treatment quality assessment results for the welded weld; establishing a multi-weight neural network model, and using the marked acoustic signal sample set to train the multi-weight neural network model; obtaining feature values of welded weld impact treatment acoustic signals to be determined, inputting the feature values into the trained multi-weight neural network model, and outputting determination results for welded weld impact treatment quality to be determined.


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