[go: up one dir, main page]

Thekkuden et al., 2019 - Google Patents

Investigation of feed-forward back propagation ANN using voltage signals for the early prediction of the welding defect

Thekkuden et al., 2019

View HTML
Document ID
5451065815478868636
Author
Thekkuden D
Mourad A
Publication year
Publication venue
SN Applied Sciences

External Links

Snippet

The research paper investigates the prediction capability of the artificial neural network for weld quality assessment from the captured voltage signals in a gas metal arc welding process. The bead-on-plate welds and v-groove welds were made on SA 516 grade 70 …
Continue reading at link.springer.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1456—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1459—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
    • B23K31/00—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups
    • B23K31/12—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups relating to investigating the properties, e.g. the weldability, of materials
    • B23K31/125—Weld quality monitoring

Similar Documents

Publication Publication Date Title
Thekkuden et al. Investigation of feed-forward back propagation ANN using voltage signals for the early prediction of the welding defect
Madhvacharyula et al. In situ detection of welding defects: A review
Dai et al. Deep learning assisted vision inspection of resistance spot welds
Pal et al. Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals
Li et al. Laser cladding state recognition and crack defect diagnosis by acoustic emission signal and neural network
Chen et al. Prediction of weld bead geometry of MAG welding based on XGBoost algorithm
Kim et al. Review on machine learning based welding quality improvement
Wang et al. A novel surface temperature sensor and random forest-based welding quality prediction model
Raj et al. Weld quality monitoring via machine learning-enabled approaches
Sumesh et al. Acoustic signature based weld quality monitoring for SMAW process using data mining algorithms
Chuenmee et al. Machine learning for predicting resistance spot weld quality in automotive manufacturing
Ho et al. An artificial neural network approach for parametric study on welding defect classification
Esther Florence et al. Artificial intelligence based defect classification for weld joints
Dhanraj et al. A credal decision tree classifier approach for surface condition monitoring of friction stir weldment through vibration patterns
Yella et al. Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing
Chen et al. Deep learning based online nondestructive defect detection for self-piercing riveted joints in automotive body manufacturing
Rameshkumar et al. Machine learning models for weld quality monitoring in shielded metal arc welding process using arc signature features
Lofinmakin et al. Prediction of optimal mild steel weld parameters using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique
Fu et al. Quality evaluation of resistance spot welding based on dynamic reactance signal and radar diagram method
Breitenbach et al. A systematic literature review on machine learning approaches for quality monitoring and control systems for welding processes
Lee et al. Study on Real-Time Porosity Defect Detection Through Neural Network Structure Optimization using Genetic Algorithm in GMAW
Arabaci et al. Weld defect categorization from welding current using principle component analysis
Boldsaikhan et al. Neural network evaluation of weld quality using FSW feedback data
Kurniadi et al. Real-time parameter adjustment and fault detection of remote laser welding by using ANN
Deng et al. Defect detection and data generation of shunt between three layers of resistance spot welding based on deep convolutional generative adversarial network and conditional variational autoencoder