Analysis of mechanical properties and optimization of gas tungsten Arc welding (GTAW) parameters on dissimilar metal titanium (6Al 4V) and aluminium 7075 by Taguchi and ANOVA techniques

Nandagopal, K.; Kailasanathan, C.

doi:10.1016/j.jallcom.2016.05.006

Cited by 60 publications

(26 citation statements)

References 21 publications

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“…(1), is exploited in this research. The failing load, which is one of input parameters, is measured for calculating mean and ratio S/N and ultimately determining the optimum input parameters for welding process [21]. The mean and the ratio S/N are determined from the variable assessment for the hot-air welding process.…”

Section: Taguchi Methodsmentioning

confidence: 99%

Analysis of Failing load and Optimization of Hot Air Welding Parameters on PVC-Acrylic Coated Polyester Fabric by Taguchi and ANOVA Technique

Khaothong¹

2019

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There are several factors affecting the strength of welding seam through hot air for PVC-acrylic coated polyester fabrics. To obtain the most durability of the welding seam for practical use, the experimental design of this study adopts Taguchi method for determining a number of experiments. Each input parameter has five different levels. The result shows that the input parameters providing maximum failing load for welding in warp and weft directions are the following: the distance between a head of blower and workpiece (Dis) of 10 mm, the temperature of hot air (Tem) of 160 o C, the torque (Tq) of 9,000 N.mm, and the speed of hot air (Sha) of 4,000 mm/s. The maximum failing loads are 95.38 kN/m and 86.17 kN/m for warp direction and weft direction, respectively. After filtering the input parameters at the significance level of 0.05, the input parameters which significantly affect the strength of welding seam are the distance between a head of blower and workpiece, and the temperature of hot air. Additionally, the relationship between the input parameters and the failing load seams can be establishad. The comparison between the actual experimental data and the predicted values of the failing load for both welding directions shows that the average percentage error is less than 0.1% so that the forecast equation is appropriate for the explanation of the variables for this study and provide statistical reliability.

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Section: Taguchi Methodsmentioning

confidence: 99%

Analysis of Failing load and Optimization of Hot Air Welding Parameters on PVC-Acrylic Coated Polyester Fabric by Taguchi and ANOVA Technique

Khaothong¹

2019

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“…Variance is the measure as to find how every individual widely vary in a group. The analysis of variance provides the contribution of each parameter towards the inappropriate performance [20]. This is done by incorporating the sum of squares method, where Sum of Squares (SS) is the sum of squared differences from the mean of the grey relational grade( ).…”

Section: Analysis Of Variance (Anova)mentioning

confidence: 99%

Multi-objective Optimization of Friction Welding Process Parameters using Grey Relational Analysis for Joining Aluminium Metal Matrix Composite

Sundararajan

Shanmugam²

2018

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Aluminium metal matrix composites has gained importance in recent time because of its improved mechanical and metallurgical properties. The welding of aluminium metal matrix composites using conventional welding process has got many demerits so in order to overcome them a solid state welding process is to be employed. To achieve a good strength, weld in the aluminium metal matrix composite bars an efficient and most preferred technique is friction welding. In this work the aluminium metal matrix composite AA7075 + 10 % vol SiC-T6 is selected and friction welded. The combination of friction welding process parameters such as spindle speed, friction pressure, upset pressure and burn-off-length for joining the AA7075 + 10 % vol SiCP-T6 metal matrix composite bars are selected by Taguchi's design of experiment. The optimum friction welding parameters were determined for achieving improved ultimate tensile strength and the hardness using grey relational analysis. A combined grey relational grade is found from the determined grey relational coefficient of the output responses and the optimum friction welding process parameters were obtained as spindle speed-1200 rpm, friction pressure-100 MPa, upset pressure-250 MPa, Burn-off-Length-2 mm. Analysis of variance (ANOVA) performed shows that the friction pressure is the most significant friction welding parameter that influences the both the ultimate tensile strength and hardness of friction welded AA7075 + 10 % volSiCP-T6 joints. The fractured surface under microstructure study also revealed good compliance with the grey relational grade result.

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“…In addition, a statistical ANOVA was used for each response individually to see which process parameters had statistically major impacts on the responses. The optimal combination of the process parameters can then be predicted [10,[28][29][30][31]. Figure 2.…”

Section: Experimental Details Materials and Testingmentioning

confidence: 99%

“…Arun Kumar et al carried out multiresponse optimisation process parameters for TIG welding of Incoloy 800HT by Taguchi grey relational analysis [27]. Nandagopal et al analysed the mechanical properties and optimisation of gas tungsten arc welding (GTAW) parameters on dissimilar metal titanium (6Alsingle bond4V) and aluminium 7075 by the Taguchi and ANOVA techniques [28]. Shanmugarajan et al have optimised the laser welding parameters for welding of P92 material using Taguchi based grey relational analysis [29].…”

Section: Introductionmentioning

confidence: 99%

Investigation on TIG welding parameters of 2205 duplex stainless steel

Ravichandran¹,

Sait²,

Vignesh³

2017

Int. J. Automot. Mech. Eng.

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TIG welding process parameters were analysed for joining duplex stainless-steel plates. Signal to noise (SN) ratio and Analysis of Variance (ANOVA) analyses were applied for investigating the selected welding parameters. The selected parameters were current (A), gas flow rate (L/min) and speed (mm/min). The purpose of this work is to produce weld joints with maximum impact of strength and hardness. L9 orthogonal array was selected according to the aforementioned factors with three levels. The impact strength of the joints was found using the Izord impact testing machine and hardness of all joints was measured using the Brinell Hardness machine. From the SN ratio analysis, it was observed that for the high impact strength, the required welding current was 150 A, gas flow rate was 14 L/min and the welding speed was 210 mm/min while for high hardness, the welding current was 190 A, gas flow rate was 12 L/min and the welding speed was 175 mm/min. ANOVA analysis indicated that the gas flow rate was the most significant one for both impact strength and hardness of the joints.

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Analysis of mechanical properties and optimization of gas tungsten Arc welding (GTAW) parameters on dissimilar metal titanium (6Al 4V) and aluminium 7075 by Taguchi and ANOVA techniques

Cited by 60 publications

References 21 publications

Analysis of Failing load and Optimization of Hot Air Welding Parameters on PVC-Acrylic Coated Polyester Fabric by Taguchi and ANOVA Technique

Analysis of Failing load and Optimization of Hot Air Welding Parameters on PVC-Acrylic Coated Polyester Fabric by Taguchi and ANOVA Technique

Multi-objective Optimization of Friction Welding Process Parameters using Grey Relational Analysis for Joining Aluminium Metal Matrix Composite

Investigation on TIG welding parameters of 2205 duplex stainless steel

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