Monitoring of back bead penetration based on temperature sensing and deep learning-Reference-Cited by-同舟云学术

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Monitoring of back bead penetration based on temperature sensing and deep learning

Published:2022-01 Issue: Volume:188 Page:110410
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Yu Rongwei,He Huiying,Han Jing,Bai Lianfa,Zhao Zhuang,Lu Jun

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference43 articles.

1. Weld quality monitoring research in small scale resistance spot welding by dynamic resistance and neural network;Wan;Measurement,2017

2. Monitoring of weld joint penetration during variable polarity plasma arc welding based on the keyhole characteristics and PSO-ANFIS;Wu;J. Mater. Process. Technol.,2017

3. Deep features based on a DCNN model for classifying imbalanced weld flaw types;Hou;Measurement,2019

4. Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH;Peng;Measurement,2020

5. Estimation of Weld Joint Penetration Under Varying GTA Pools;Liu;Welding Journal,2013

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4. Rear Weld Pool Thermal Monitoring in GTAW Process Using a Developed Two-Colour Pyrometer;Metals;2024-08-16

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