Name
Title | ||
|---|---|---|
Multi-sensor based prediction of metal deposition in pulsed gas metal arc welding using various soft computing models |
Abstract | ||
|---|---|---|
The deposition efficiency is an important economic factor in welding. A multitude of uncontrollable factors influence the metal deposition, which indicates the necessity of robust sensors with an intelligent system to monitor the process in real time. This paper attempts to develop artificial neural network (ANN) models to predict the weld deposition efficiency using the welding sound signal along with the welding current and the arc voltage signals in pulsed metal inert gas welding. Three different implementations of ANNs have been used: gradient descent error back-propagation, neuro-genetic algorithm and neuro-differential evolution. The results indicate that the sound signal kurtosis, used in conjunction with the current and the voltage signals, is a reliable indicator of deposition efficiency. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.asoc.2011.08.016 | Appl. Soft Comput. |
Keywords | Field | DocType |
sound signal kurtosis,pulsed metal inert gas,voltage signal,arc voltage signal,various soft computing model,weld deposition efficiency,welding sound signal,deposition efficiency,pulsed gas metal arc,different implementation,metal deposition,artificial neural network,back propagation,differential evolution,genetic algorithm | Gradient descent,Mathematical optimization,Voltage,Mechanical engineering,Hybrid neural network,Soft computing,Gas metal arc welding,Backpropagation,Mathematics,Welding,Inert gas | Journal |
Volume | Issue | ISSN |
12 | 1 | 1568-4946 |
Citations | PageRank | References |
3 | 0.42 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandip Bhattacharya | 1 | 3 | 0.76 |
| Kamal Pal | 2 | 8 | 1.62 |
| Surjya K. Pal | 3 | 58 | 9.70 |