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ISSN 2229-5518
PARAMETRIC OPTIMIZATION OF TIG WELDING PARAMETERS USING TAGUCHI
METHOD FOR DISSIMILAR JOINT
(Low carbon steel with AA1050)
J.Pasupathy, V.Ravisankar
Index Terms— TIG welding, optimization, orthogonal array, S/N ratio
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UNGSTEN Inert Gas welding is one of the most widely used processes in industry. The input parameters play a very significant role in determining the quality of a welded
joint. In fact, weld geometry directly affects the complexity of weld schedules and thereby the construction and manufactur- ing costs of steel structures and mechanical devices. Therefore, these parameters affecting the arc and welding should be esti- mated and their changing conditions during process must be known before in order to obtain optimum results; in fact a per- fect arc can be achieved when all the parameters are in confor- mity. These are combined in two groups as first order adjusta- ble and second order adjustable parameters defined before welding process. Former are welding current, welding speed and distance between the electrode and workpiece. These pa- rameters will affect the weld characteristics to a great extent. Because these factors can be varied over a large range, they are considered the primary adjustments in any welding operation. Their values should be recorded for every different type of weld to permit reproducibility. Ugur Esme [45] an investigation of the effect and optimization of welding parameters on the tensile shear strength in the resistance spot welding (RSW) process- conducted experimental studies under varying electrode forces, welding currents, electrode diameters, and welding times. K. Kishore, P. V. Gopal Krishna, K. Veladri and Syed Qasim Ali [17] worked on welding of materials like steel and is still critical and ongoing. Sourav Datta, Ajay Biswas, Gautam Majumdar [39] worked on Sensitivity Analysis. It has been carried out to check the case sensitiveness of relation importance of different bead geometry parameters imposing predominant effect on the optimal parametric combination. P K Palani, Dr N Murugan,
J.Pasupathy, Research Scholar, Department of Manufacturing Engineer- ing, Annamalai University, Annamalai Nagar, Chidambaram, Tamilna- du, India. PH- +91 9791564551E-mail: jkpasu@rediffmail.com
V.Ravisankar, Professor, Department of Manufacturing Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamilnadu,
India, PH-+91 9965538727. E-mail:pravicdm@yahoo.co.in
[31] designed the DOE using Taguchi approach can significant- ly reduce time required for experimental investigations [29,37,39]. In this investigation, Taguchi's orthogonal arrays were used to conduct the experiments to find the contributions of each factor and to optimize the parameter settings.
Taguchi Technique is applied to plan the experiments. The Taguchi method has become a powerful tool for improving productivity during research and development, so that high quality products can be produced quickly and at low cost. Dr. Taguchi of Nippon Telephones and Telegraph Company, Ja- pan has developed a method based on "ORTHOGONAL AR- RAY" experiments which gives much reduced "variance" for the experiment with "optimum settings" of control parameters. Thus the combination of Design of Experiments with optimi- zation of control parameters to obtain best results is achieved in the Taguchi Method. "Orthogonal Arrays" (OA) provide a set of well balanced (minimum) experiments and Dr. Tagu- chi's Signal-to-Noise ratios (S/N), which are log functions of desired output, serve as objective functions for optimization, help in data analysis and prediction of optimum results.
Signal-to-Noise Ratio
There are 3 Signal-to-Noise ratios of common interest for op-
timization
(i) Smaller-The-Better:
n = -10 Log10 [mean of sum of squares of measured data]
(ii) Larger-The-Better:
n = -10 Log10 [mean of sum squares of reci- procal of measured data]
(iii) Nominal-The-Best:
n = 10 Log10 square of mean variance
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1mm thick Low carbon steel and 2mm thick AA1050 alu- minium alloy were used. The dimensions of the work piece, length 300 mm, width 150mm. For selection of workpiece, refer- ence of the procedure handbook of Arc Welding & Welding Process Technology by P. T. Houldcroft is referred. This expe- riment, TIG welding is done using Lincoln machine, Polarity: Direct Current Electrode Negative [DCEN], Welding current, welding speed and distance of electrode from workpiece are
130, 135, 140Amps, 3.2, 3.5, 3.8mm/sec and 2.3, 2.4, 2.5mm re- spectively, Voltage 16V, Frequency 60Hz, The arc distance, elec- trode type, electrode size and electrode tip angle were 2.4mm, EWTh-2, 3mm in diameter and Vertical respectively. Pure argon gas with 20L/min was used for preventing oxidation of molten steel.
Taguchi‘s orthogonal design uses a special set of prede- fined arrays called orthogonal arrays (OAs) to design the plan of experiment. These standard arrays stipulate the way of full information of all the factors that affects the process perfor- mance (process responses). The corresponding OA is selected from the set of predefined OAs according to the number of fac- tors and their levels that will be used in the experiment. Below Table No.2 shows L9 Orthogonal array from Table1.
In the Taguchi Method the term ‗signal‘ represents the de- sirable value (Mean) for the output characteristic and the term
‗noise‘ represents the undesirable value (Standard Deviation)
for the output characteristic. Therefore, the S/N ratio to the
mean to the S. D. S/N ratio used to measure the quality charac- teristic deviating from the desired value. In S/N ratio, S is de- fined as
quality characteristic for strength must be taken. The M.S.D. for higher-the –better quality characteristic can be expressed as,
M.S.D = ∑ 1/Pi2
Where, Pi is the value of penetration.
Regardless of the category of the quality characteristic, a greater S/N ratio corresponds to better quality characteristics. Therefore, the optimal level of the process parameters is the level with the greatest S/N ratio. The S/N response table for strength is shown in Table No.4 as below
The purpose of the analysis of variance (ANOVA) is to investigate which design parameters significantly affect the quality characteristic. This is accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean S/N ratio, into contributions by each of the design parameters and the error. First, the total sum of squared deviations SST from the total mean S/N ratio nm can be calculated as,
SST = ∑(ni - nm)2
S= -10 log (M.S.D.)
where, M.S.D. is the Mean Square Deviation for the output
characteristic.
To obtain optimal welding performance, higher-the better
Once the optimal level of design parameters has been se- lected, the final step is to predict and verify the improvement of the quality characteristic using the optimal level of design pa-
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rameters. The estimated S/N ratio using the optimal level of the design parameters can be calculated as
n
ή = ηm + ∑i=1 (ηi - ηm)
where ηm is total mean of S/N ratio, ηi is the mean of S/N ratio at the optimal level, and n is the number of main welding pa- rameters that significantly affect the performance. The compari- son of the predicted strength with actual strength using the op- timal parameters is shown in table 6. Good agreement between the predicted and actual penetration being observed.
Taguchi optimization method was applied to find the op- timal process parameters for strength. A Taguchi orthogonal array, the signal-to-noise (S/N) ratio and analysis of variance were used for the optimization of welding parameters. A con- formation experiment was also conducted and verified for the effectiveness of the Taguchi optimization method. The experi- ment value that is observed from optimal welding parameters, the strength is 61.37MPa. & S/N ratio is 16.45.
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