Optimizing Pulsed Current Micro Plasma Arc Welding Parameters to Maximize Ultimate Tensile Strength of AISI 304L Sheets Using Response Surface Method

Abstract:

AISI 304L is an austenitic Chromium-Nickel stainless steel offering the optimum combination of corrosion resistance, strength and ductility. These attributes make it a favorite for many mechanical components. The paper focuses on developing mathematical model to predict ultimate tensile strength of pulsed current micro plasma arc welded AISI 304L joints. Four factors, five level, central composite rotatable design matrix is used to optimize the number of experiments. The mathematical model has been developed by response surface method. The adequacy of the model is checked by ANOVA technique. By using the developed mathematical model, ultimate tensile strength of the joints can be predicted with 99% confidence level. Contour plots are drawn to study the interaction effect of pulsed current micro plasma arc welding parameters ultimate tensile strength of AISI 304L steel. The developed mathematical model has been optimized using Response Surface Method to maximize the ultimate tensile strength.

• Periodical: Applied Mechanics and Materials (Volume 660)
• Pages: 322-326
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.660.322
• Online since: October 2014
• Authors: Kondapalli Siva Prasad*, Chalamalasetti Srinivasa Rao, Damera Nageswara Rao
• Keywords: AISI 304L, ANOVA, Design of Experiment (DOE), Pulsed Current Micro Plasma Arc Welding, Response Surface Method (RSM), Ultimate Tensile Strength (UTS)

References

[1] M. Balasubramanian, V. Jayabalan, V. Balasubramanian, Effect of process parameters of pulsed current tungsten inert gas welding on weld pool geometry of titanium welds, Acta Metall. Sin. (Engl. Lett. ) 23 (2010) 312-320.

DOI: 10.1115/1.4000373

[2] B. Balasubramanian, V. Jayabalan, V. Balasubramanian, Optimizing the Pulsed Current Gas Tungsten Arc Welding Parameters, J Mater Sci Technol. 22 (2006) 821-825.

DOI: 10.1007/bf03027891

[3] G. Madusudhana Reddy , A. A. Gokhale, K. Prasad Rao, Weld microstructure refinement in a 1441 grade aluminium-lithium alloy, Journal of Material Science, 32 (1997) 4117-4126.

[4] D .K. Zhang and J. T. Niu , Application of Artificial Neural Network modeling to Plasma Arc Welding of Aluminum alloys, Journal of Advanced Metallurgical Sciences, 13 (2000) 194-200.

[5] Sheng-Chai Chi, LI-Chang Hsu , A fuzzy Radial Basis Function Neural Network for Predicting Multiple Quality characteristics of Plasma Arc Welding, IEEE, 0-7803-7078-3/01 (2001) 2807-2812.

DOI: 10.1109/nafips.2001.943671

[6] Y. F. Hsiao, Y. S. Tarng, and J. Wang Huang, Optimization of Plasma Arc Welding Parameters by Using the Taguchi Method with the Grey Relational Analysis, Journal of Materials and Manufacturing Processes, 23 (2008) 51–58.

DOI: 10.1080/10426910701524527

[7] Siva. K, Muragan. N, Logesh. R, Optimization of weld bead geometry in Plasma transferred arc hardfacing austenitic stainless steel plates using genetic algorithm, Int J Adv Manuf Technol. 41 (2008) 24-30.

DOI: 10.1007/s00170-008-1451-3

[8] A. K. Lakshinarayana, V. Balasubramanian, R. Varahamoorthy and S. Babu, Predicted the Dilution of Plasma Transferred Arc Hardfacing of Stellite on Carbon Steel using Response Surface Methodology, Metals and Materials International, 14 (2008).

DOI: 10.3365/met.mat.2008.12.779

[9] Kondapalli Siva Prasad, Ch. Srinivasa Rao, D. Nageswara Rao, Review on application of Response Surface Method based Design of Experiments to Welding processes, Journal of Manufacturing Science & Production (De Gruyter), 12 (2012) 17-24.

DOI: 10.1515/jmsp-2011-0010

[10] Kondapalli Siva Prasad, Ch. Srinivasa Rao, D. Nageswara Rao, Application of Design of Experiments to Plasma Arc Welding: A Review, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 35 (2012) 75-81.

DOI: 10.1515/jmsp-2011-0010