[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Huang et al., 2019 - Google Patents

Penetration estimation of GMA backing welding based on weld pool geometry parameters

Huang et al., 2019

View HTML @Full View
Document ID
3430020938043142178
Author
Huang J
Xue L
Huang J
Zou Y
Ma K
Publication year
Publication venue
Chinese Journal of Mechanical Engineering

External Links

Snippet

Penetration estimation is a prerequisite of the automation of backing welding based on vision sensing technology. However, the arc interference in welding process leads to the difficulties of extracting the weld pool characteristic information, which brings great …
Continue reading at link.springer.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination

Similar Documents

Publication Publication Date Title
Xia et al. A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system
Chen et al. Welding penetration monitoring for pulsed GTAW using visual sensor based on AAM and random forests
Lei et al. Real-time weld geometry prediction based on multi-information using neural network optimized by PCA and GA during thin-plate laser welding
Han et al. A structured light vision sensor for on-line weld bead measurement and weld quality inspection
Yang et al. An automatic detection and identification method of welded joints based on deep neural network
Huang et al. Penetration estimation of GMA backing welding based on weld pool geometry parameters
Zhao et al. Additive seam tracking technology based on laser vision
Cai et al. Real-time laser keyhole welding penetration state monitoring based on adaptive fusion images using convolutional neural networks
Stavridis et al. A cognitive approach for quality assessment in laser welding
Pacher et al. Real-time continuous estimation of dross attachment in the laser cutting process based on process emission images
Hong et al. Filter-PCA-based process monitoring and defect identification during climbing helium arc welding process using DE-SVM
Yu et al. Monitoring of butt weld penetration based on infrared sensing and improved histograms of oriented gradients
Huang et al. Improved convolutional neural network for laser welding defect prediction
Geng et al. A method of welding path planning of steel mesh based on point cloud for welding robot
Jin et al. 3D reconstruction of GMAW pool surface using composite sensor technology
Alcaraz et al. Indirect porosity detection and root-cause identification in WAAM
Hong et al. AF-FTTSnet: An end-to-end two-stream convolutional neural network for online quality monitoring of robotic welding
Nogay et al. Classification of operation cases in electric arc welding wachine by using deep convolutional neural networks
Cai et al. Monitoring of distance between laser beam and arc in laser-arc hybrid welding based on deep learning
Couto et al. Mapping of bead geometry in wire arc additive manufacturing systems using passive vision
Gao et al. Method for monitoring and controlling penetration of complex groove welding based on online multi-modal data
Baek et al. Optimization of weld penetration prediction based on weld pool image and deep learning approach in gas tungsten arc welding
Zhang et al. On-line monitoring and defects detection of robotic arc welding: A review and future challenges
Dellarre et al. Qualify a NIR camera to detect thermal deviation during aluminum WAAM
Liu et al. ANFIS Modeling of Human Welder's Response to Three-Dimensional Weld Pool Surface in GTAW