Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks

By D. Nagesh, G. L. Datta. 2002

316 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/50ce4c04fb1ce7c6430ded2e543ba091a161d141

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanical Engineering

1. A. Olabi
2. K. Benyounis
3. Jun Xiong
4. Guangjun Zhang
5. Lin Wu
6. Huijun Li
7. Zengxi Pan
8. Donghong Ding

In Mechanics of Materials

1. K. Benyounis
2. A. Olabi

Citations in the corpus

• Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis (2012, background): “Nagesh and Datta (2002) investigated the process variables selection for the optimization of bead geometry through neural networks in shielded metal-arc welding.”
• Optimization of different welding processes using statistical and numerical approaches - A reference guide (2008, methodology): “Usage of ANN to model the GMAW process was reported by Nagesh and Datta [31].”