Sensitivity model for prediction of bead geometry in underwater wet flux cored arc welding

To investigate influence of welding parameters on weld bead geometry in underwater wet flux cored arc welding (FCAW), orthogonal experiments of underwater wet FCAW were conducted in the hyperbaric chamber at water depth from 0.2 m to 60 m and mathematical models were developed by multiple curvilinear regression method from the experimental data. Sensitivity analysis was then performed to predict the bead geometry and evaluate the influence of welding parameters. The results reveal that water depth has a greater influence on bead geometry than other welding parameters when welding at a water depth less than 10 m. At a water depth deeper than 10 m, a change in travel speed affects the bead geometry more strongly than other welding parameters.

[1]  N. Murugan,et al.  Development of mathematical models for prediction of weld bead geometry in cladding by flux cored arc welding , 2006 .

[2]  Huang Jin.  Arc stability of underwater wet flux-cored arc welding , 2012 .

[3]  Ill-Soo Kim,et al.  An investigation into an intelligent system for predicting bead geometry in GMA welding process , 2005 .

[4]  Serdar Karaoğlu,et al.  Sensitivity analysis of submerged arc welding process parameters , 2008 .

[5]  Kamal Pal,et al.  Soft computing methods used for the modelling and optimisation of Gas Metal Arc Welding: a review , 2011, Int. J. Manuf. Res..

[6]  Young-Soo Yang,et al.  Sensitivity analysis for process parameters in GMA welding processes using a factorial design method , 2003 .

[7]  A. Noorul Haq,et al.  Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm , 2012, Journal of Intelligent Manufacturing.

[8]  Surjya K. Pal,et al.  Optimization of quality characteristics parameters in a pulsed metal inert gas welding process using grey-based Taguchi method , 2009 .

[9]  N. Murugan,et al.  Sensitivity Analysis for Process Parameters in Cladding of Stainless Steel by Flux Cored Arc Welding , 2006 .

[10]  A. Noorul Haq,et al.  Optimizing the weld bead characteristics of super austenitic stainless steel (904L) through grey‐based Taguchi method , 2010 .

[11]  Ill-Soo Kim,et al.  Sensitivity analysis for process parameters influencing weld quality in robotic GMA welding process , 2003 .

[12]  V. Balasubramanian,et al.  Effect of pulsed current on temperature distribution, weld bead profiles and characteristics of gas tungsten arc welded aluminum alloy joints , 2011 .

[13]  Madavan Vasudevan,et al.  Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool , 2012, Metallurgical and Materials Transactions B.

[14]  Y. S. Tarng,et al.  Optimisation of the weld bead geometry in gas tungsten arc welding by the Taguchi method , 1998 .

[15]  N. Murugan,et al.  Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel , 1994 .

[16]  G. L. Datta,et al.  Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process , 2010, Appl. Soft Comput..

[17]  O. P. Gupta,et al.  Effect of process parameters and mathematical model for the prediction of bead geometry in pulsed GMA welding , 2009 .

[18]  E. Siores,et al.  Mathematical models for control of weld bead penetration in the GMAW process , 1996 .

[19]  Jose A. Ventura,et al.  A structural model for shape recognition using neural nets , 1996, J. Intell. Manuf..

[20]  Malcolm Bibby,et al.  Modelling Gas Metal Arc Weld Geometry Using Artificial Neural Network Technology , 1999 .