Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes

By N. Murugan, V. Gunaraj. 2005

221 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/59db63f3d6be03efdc54152367fb9fd216f5dab7

How researchers see this paper

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

In Industrial and Manufacturing Engineering

1. Jun Xiong
2. Guangjun Zhang
3. Lin Wu

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

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): “Murugan and Gunaraj (2005) developed the relationship between weld bead width, height and penetration with process parameters for submerged arc welding pipes.”
• Optimization of different welding processes using statistical and numerical approaches - A reference guide (2008, background): “Kannan and Murugan [24] have studied the effect of flux cored arc welding process parameters (welding current, welding speed, nozzle-to-plate distance and welding torch angle with reference to vertical) on the duplex stainless steel clad quality in terms of penetration, width, reinforcement and perc”