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

Koleva et al., 2017 - Google Patents

Neural networks for defectiveness modeling at electron beam welding

Koleva et al., 2017

View PDF
Document ID
2020201855710837641
Author
Koleva L
Koleva E
Publication year
Publication venue
Industry 4.0

External Links

Snippet

This paper considers the process electron beam welding in vacuum of stainless steel 1H18NT. Neural network based models are developed and used for the description of the defectiveness, depending on the process parameters-electron beam power, welding …
Continue reading at stumejournals.com (PDF) (other versions)

Similar Documents

Publication Publication Date Title
Sampson et al. An improved methodology of melt pool monitoring of direct energy deposition processes
Pal et al. Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals
Desai et al. Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches
Kim et al. Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing
Ismail et al. Neural network modeling for prediction of weld bead geometry in laser microwelding
Hu et al. Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing
US20230141266A1 (en) Systems and Methods
Ramos-Jaime et al. Effect of process parameters on robotic GMAW bead area estimation
Irgolic et al. Prediction of cutting forces with neural network by milling functionally graded material
Tang et al. An online surface defects detection system for AWAM based on deep learning
Peko et al. Modelling of Kerf width in plasma jet metal cutting process using ANN approach
Koleva et al. Neural networks for defectiveness modeling at electron beam welding
Milaat et al. Prediction of melt pool geometry using deep neural networks
Peko et al. Modeling of surface roughness in plasma jet cutting process of thick structural steel
Tsonevska et al. Modelling the shape of electron beam welding joints by neural networks
Casalino et al. Neuro‐Fuzzy Model for the Prediction and Classification of the Fused Zone Levels of Imperfections in Ti6Al4V Alloy Butt Weld
Mollah et al. Modeling of TIG welding and abrasive flow machining processes using radial basis function networks
Knüttel et al. Height prediction in directed metal deposition with artificial neural networks
Rahman et al. A Machine Learning Approach for Predicting Melt-Pool Dynamics of Ti-6Al-4V Alloy in the Laser Powder-Bed Fusion Process
Chandra et al. Deep learning for anomaly detection in wire-arc additive manufacturing
Aggarwal et al. A methodology for investigating and modelling laser clad bead geometry and process parameter relationships
Koleva et al. GRAPHICAL USER INTERFACE FOR OPTIMIZATION OF ELECTRON BEAM WELDING BY NEURAL AND REGRESSION MODELS FOR OBTAINING DEFECTFREE WELDS
Vincent et al. Machine Learning Approach to Predict Bead Height and Width in Wire Arc Additive Manufacturing Sample
Sharma et al. Intelligent modelling and multi-objective optimisation of laser beam cutting of nickel based superalloy sheet
Hilton et al. A statistics based Digital Twin for the combined consideration of heat treatment and machining for predicting distortion