Optimization of A-GTAW welding parameters for naval steel (DMR 249 A) by design of experiments approach

DMR249A steel is indigenously developed high strength low alloy (HSLA) steel. The steel is being used for construction of Indian Aircraft Carrier and other new ships under construction at various ship yards in India. In order to enhance the depth of penetration (DOP) achievable in a single pass for gas tungsten arc welding (GTAW) process, activated fluxes were developed for the steel. The process is called activated flux gas tungsten arc welding (A-GTAW). Design of experiments (DOE) approach was employed using response surface methodology (RSM) and Taguchi technique to optimize the welding parameters for achieving maximum DOP in a single pass. Design matrix was generated using DOE techniques and bead on plate experiments were carried out to generate data for influence of welding process variables on DOP. The input variables considered were current, torch speed, and arc gap. The DOP was considered as response variable. The equations correlating DOP with the process parameters were developed for both the optimization techniques. The identified optimum process parameters were validated by carrying out bead on plate experiments. The RMS error of the predicted and measured DOP values for the validation experiments of the RSM (D-optimal) and Taguchi optimization technique was found to be 0.575 and 0.860, respectively. Thus, RSM (D-optimal) was observed to predict optimized welding process parameters for achieving maximum DOP with better accuracy during A-GTAW process.

[1]  P. Chapelle,et al.  Effect of oxide fluxes on activation mechanisms of tungsten inert gas process , 2006 .

[2]  Y. S. Tarng,et al.  Process parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel , 2002 .

[3]  R. Roy.  A Primer on the Taguchi Method , 1990 .

[4]  M. Bezerra,et al.  Response surface methodology (RSM) as a tool for optimization in analytical chemistry. , 2008, Talanta.

[5]  André I. Khuri,et al.  Response surface methodology , 2010 .

[6]  C. Chou,et al.  Evaluation of TIG flux welding on the characteristics of stainless steel , 2005 .

[7]  Dennis L. Young,et al.  Application of statistical design and response surface methods to computer-aided VLSI device design II. Desirability functions and Taguchi methods , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[8]  Deniz Baş,et al.  Modeling and optimization I: Usability of response surface methodology , 2007 .

[9]  T. Paskell,et al.  GTAW flux increases weld joint penetration , 1997 .

[10]  Bernard C. Jiang,et al.  Taguchi-based methodology for determining/optimizing robot process capability , 1991 .

[11]  J. C. Warner,et al.  Molding process is improved by using the Taguchi method , 1989 .

[12]  I. Boyaci,et al.  Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction , 2007 .

[13]  Jacob Cohen.  Multiple regression as a general data-analytic system. , 1968 .

[14]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[15]  B. Mishra,et al.  Production of DMR 249A Steel at SAIL, Bokaro Steel Plant , 2012 .

[16]  Lihsin Liu,et al.  Computer-aided design for quality (CADQ) , 1990, AT&T Technical Journal.

[17]  Kwang-Jae Son,et al.  Optimization of Nd:YAG laser welding parameters for sealing small titanium tube ends , 2006 .

[18]  K. Y. Benyounis,et al.  Multi-response optimization of CO2 laser-welding process of austenitic stainless steel , 2008 .

[19]  Shankar Chakraborty,et al.  Multi-response optimisation of WEDM process using principal component analysis , 2009 .

[20]  D. Montgomery,et al.  Choice of second-order response surface designs for logistic and Poisson regression models , 2009 .

[21]  Mong-Na Lo Huang,et al.  D-Optimal Designs for Second-order Response Surface Models with Qualitative Factors , 2011 .

[22]  Bernard C. Jiang,et al.  Using Taguchi methods to determine/optimize robot process capability for path following , 1991 .

[23]  Peter Goos,et al.  D -optimal response surface designs in the presence of random block effects , 2001 .