Open Access Open Access  Restricted Access Subscription Access

Modelling and Process Optimization for Tig Welding Based On Taguchi Method and Simulated Annealing Technique

Sudip Kumar Halder, Pradip Kumar Pal, Goutam Nandi

Abstract


In the present study, emphasis has been given to perceive the effect of some decided on enter parameters at the great of butt-welded joints, even as welding of chrome steel with the aid of tungsten inert gas (TIG) welding. Experiments had been carried out as consistent with L9 orthogonal array of Taguchi method. The enter parameters considered in the take a look at are: cutting-edge, gasolines go with the flow fee and arc gap. Statistical strategies analysis of variance (ANOVA), signal-to-noise (S/N) ratio and major impact plots have been used to study the results of welding parameters on remaining tensile energy of weld specimen. Simulated annealing has been used to optimize the welding parameter to maximize last tensile energy of weld specimen. From the effects of this study, gasoline flow rate is recognized because the maximum influential welding parameter on final tensile strength. Confirmatory test has been conducted to validate the predicted setting.


Full Text:

PDF

References


S. Nagesh, L. G. Datta, “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”, Applied soft Computing, 10 (2010), pp. 897-907.

A. Dargutlu, “Experimental investigation of the effect of hydrogen in argon as a shielding gas on TIG welding of austenitic stainless steel”, Materials and design 25, (2004), pp. 19-23.

Y.S. Tarng and W.H. Yang, “optimization of the weld bead geometry in the gas tungsten arc welding by the Taguchi method”,

International Journal of Advanced Manufacturing Technology, 14, (1998), pp. 549-554.

S. Y. Tarng, L. H. Tsai, S. S. Yeh, “Modeling, optimization and classification of weld quality in tungsten inert gas welding”,

International Journal of Machine Tool & Manufacture, 39, (1999), pp. 1427-1438.

P. Dutta, D. K. Pratihar, “Modeling of TIG welding process using conventional regression analysis and neural l network-based approaches”, Journal of Materials Processing Technology, 184, (2007), pp. 56-68.

Kumar and S. Sundarranjan, “Optimization of pulsed TIG welding process parameters on mechanical properties of aa5456 aluminium alloy weldments”, Materials and Design, 30, (2209), pp. 1288-1297.

Zhang, G. Zhao, H.Chen, Y. Guan and H. Li, “Optimization of an aluminium profile extrusion process based on Taguchi method with S/N analysis”, International Journal of Manufaturing Technology, 60, (2012), pp. 589-599.

U.S. Patil and M.S. Kadam, “Effect of welding process parameters in MMAW for joining of dissimilar metals and parameter optimization using artificial neural fuzzy interface system”, Internal Journal of Mechanical Engineering and Technology, 4, (2013), pp. 79-85.

P. Modenesi, E. Âquio R. Apolina Ârio, Iaci M. Pereira, “TIG welding with single-component fluxes”, Journal of Materials Processing Technology 99, (2000), pp. 260 - 265.

R. Yilmaz and H. Uzun, “Mechanical properties of austenitic stainless steels welded by GMAW and GTAW”, Journal of Marmara for Pure and Aplied Sciences, 18, (2002), pp. 97-113.


Refbacks

  • There are currently no refbacks.