Optimization of Bead Geometry in CO2 Laser Welding of Ti 6Al 4V Using Response Surface Methodology
Ali Khorram, Majid Ghoreishi, Mohammad Reza Soleymani Yazdi, Mahmood Moradi
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DOI: 10.4236/eng.2011.37084   PDF    HTML     6,246 Downloads   11,319 Views   Citations

Abstract

In the presented study, the laser butt-welding of Ti 6Al 4V is investigated using 2.2 kw CO2 laser. Ti 6Al 4V alloy has widespread application in various fields of industries including the medical, nuclear and aerospace. In this study, Response Surface Methodology (RSM) is employed to establish the design of experiments and to optimize the bead geometry. The relationships between the input laser-welding parameters (i.e. laser power, welding speed and focal point position) and the process responses (i.e. welded zone width, heat affected zone width, welded zone area, heat affected zone area and penetration depth) are investigated. The multi-response optimizations are used to optimize the welding process. The optimum welding conditions are identified in order to increase the productivity and minimize the total operating cost. The validation results demonstrate that the developed models are accurate with low percentages of error (less than 12.5%).

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Khorram, A. , Ghoreishi, M. , Yazdi, M. and Moradi, M. (2011) Optimization of Bead Geometry in CO2 Laser Welding of Ti 6Al 4V Using Response Surface Methodology. Engineering, 3, 708-712. doi: 10.4236/eng.2011.37084.

1. Introduction

The laser beam focuses on a small area and creates high-power density to the material surface, thus its beam can be used as a welding process [1].

Since 1940S, titanium and its alloys have been one of the most important engineering materials which have been widely used in industries because of their specific physical, chemical and mechanical properties. High strength to weight ratio, good corrosion resistance and suitable adaptability to human body lead to its application in medical, chemistry and aerospace industries. Among titanium alloys, Ti6Al4V with a + b phase is widely used [2].

To decrease cost and time, it is comfortable to predict laser welding parameters based on process modeling. There are various methods for predicting and optimizing of the welding parameters. Recently, design of experiment (DOE) has been used for many applications in different areas. Responses surface method (RSM) is the best known type of DOE design; the concept of RSM was introduced in the early 1950’s by Box and Wilson [3].

Boumerzoug et al. studied the effect of arc welding on microstructures and mechanical properties of industrial low carbon steel. The results indicate that microstructures are different in various zones. Also maximum hardness value is situated in weld metal and heat affected zone areas [4].

Dissimilar Welding of Superduplex Stainless Steel/ HSLA Steel were investigated by Mendoza et al. The dissimilar joint has acceptable properties which are superior to the HSLA and lower than the SDSS [5].

Olabi et al. applied RSM to investigate the effect of laser welding parameters on residual stress distribution over the depth, at three locations from the weld centre line of AISI304 butt joints [6].

Casalino et al. investigated butt welding of Ti 6Al 4V alloy by using continuous CO2 laser [7]. Olabi et al. used an ANN and Taguchi algorithms integrated approach to the optimization of CO2 laser welding of medium carbon steel [8].

For butt welding of 3 mm thick Ti6Al4V alloy sheets, Akman et al. used a Nd:YAG laser with 0.3 - 50 ms pulse time and 500 Hz maximum repetition rate. Pulse energy and pulse duration were considered as variables and other parameters (repetition rate, welding speed, focal point position and gas pressure) assumed constant. Tensile strength, microhardness and weld geometrical dimension were considered as process responses [9].

Li et al. used a 6 KW CO2 laser for welding of titanium. Helium and Argon gases were used to prevent oxidation [10]. Anava et al. in their investigation used a CO2 continuous laser welding for joining a dissimilar AISI 316 stainless-steel and AISI 1009 low carbon steel plates. Taguchi approach was used as statistical design of experiment technique for optimization of welding parameters (laser power, welding speed and defocusing distance) with the objective of producing welded joint with complete penetration, minimum fusion zone size and acceptable welding profile [11].

In the present study, firstly RSM is employed for development of mathematical models. Second aim is to find the optimal welding combination that would maximize the penetration depth while minimizing other bead geometry (welded zone width, heat affected zone width, welded zone area and heat affected zone area). The laser-welding parameters used in this study were parameters that can be controlled on the welding machine.

2. Response Surface Methodology

Response Surface Methodology is one of the optimization techniques in describing the performance of the welding process and finding the optimum setting of parameters. RSM is a mathematical-statistical method that used for modeling and predicting the response of interest affected by some input variables to optimize the response [12].

RSM also specifies the relationships among one or more measured responses and the essential controllable input factors [13].

When all independent variables are measurable, controllable and continuous in the process, with negligible error, the response surface model is as follow:

(1)

where “n” is the number of independent variables.

To optimize the response “y”, it is necessary to find an appropriate approximation for the true functional relationship between the independent variables and the response surface. Usually a second-order polynomial Equation (2) is used in RSM.

(2)

3. Experimental Design

A central composite design including five levels of factors was employed. Linear and second order polynomials were fitted to the experimental data to obtain the regression equations. The lack of fit test, variance test and other adequacy measures were used in selecting optimum models. Laser power, welding speed and focal point position considered as independent input variables. Table 1 shows laser input variables and experiment levels.

4. Experimental Work

Titanium alloy Ti6Al4V with chemical composition presented in Table 2 was used as work piece material. The size of each sample was 85 mm long × 35 mm width with thickness of 1.7 mm.

To determine the working levels of each variable, several preliminary experiments were conducted. Absence of visible welding defects and at least half depth penetration were the criteria of choosing the working ranges. To avoid any systematic error, experiments were conducted in random order using an Optimo model CO2 laser machine, provided by OPTIMA Industries. Argon gas with constant pressure of 0.1 bar was used as shielding gas. For metallography of specimens, each transverse section of specimen was mounted. Etch solvent with the chemical composition of 2 ml HF + 10 ml HNO3 + 88 ml deionized H2O was employed.

Welding geometrical parameters were measured using optical microscope and image Analyzer software. The designed experiments are shown in Table 3. Figure 1 presents the bead shape and size of the selected sample.

5. Results and Discussion

5.1. Development of Mathematical Models

The geometry of weld bead was measured in accordance with parameter setting in Table 3. Statis-tical analysis

Table 1. Laser input variable and experimental levels.

Table 2. Chemical composition of alloy Ti6Al4V.

Table 3. Design matrix with code independent process variables.

indicates that welding speed and laser power are effective and focal point position has no influence on the developed models.

Considering welded zone width, statistical analysis shows that welding speed, laser power, the second order effect of welding speed and second order effect of laser power are the significant model terms. In HAZ width model, welding speed, laser power, second order effect of welding speed, second order effect of laser power and the interaction of welding speed and laser power are the most important terms in the model.

Welding speed, laser power and second order effect of welding speed are significant in welding zone area and HAZ area models.

The final developed models in terms of significant coded factors are shown below:

(3)

Figure 1. Shows the bead shape, width and penetration depth of the selected sample.

(4)

(5)

(6)

5.2. Optimization

Simultaneous optimization of multiple responses involves first building appropriate response surface model for each response and then trying to find a set of operating conditions that in some sense optimizes all response or at least keeps them in desired ranges.

In this study, process optimization was carried out by RSM method considering simultaneously five responses (welded zone width, heat affected zone width, welded zone area, heat affected zone area and penetration depth).

The criterion for perfect welding was chosen when penetration depth was 1727 µm Based on experimental results. Welded zone width, heat affected zone width, heat affected zone area and welded zone area were set equal 1380 µm, 2060 µm, 167 × 104 µm2 and 1158 × 103 µm2, respectively, as can be seen in Table 4. Process optimization carried out to reach to the above targets. Optimum parameter setting considering five simultaneous responses is shown in Table 5.

5.3. Validation of the Developed Models

To validate the developed models, confirmation experiments were carried out with optimum parameters setting. Table 6 summarizes the results of optimization, the actual experimental values and the percentages of error. The validation results demonstrated that the developed models are accurate as the percentages of errors are less than 12.5%. Cross section of bead at optimum setting is shown in Figure 2.

6. Conclusions

From the study the following points can be concluded:

1) RSM is an accurate technique to optimize the laser welding process in order to obtain the best bead geometry.

2) A laser power of 1837.4 W, welding speed of

Table 4. Set up of software method based on five responses.

Table 5. Optimum parameter setting considering five simultaneous respon-ses.

Table 6. Validation test results.

Figure 2. Cross section of bead at optimum setting.

254.25 cm/min and focal point position of 0.6941 mm are optimum parameters for obtaining the best bead geometry produced from Titanium alloy (Ti 6Al 4V).

3) The welding speed has a negative effect on all responses. However, the laser power has appositive effect on them. The focal point position has no effect on bead geometry of titanium alloy Ti 6Al 4V welding.

4) Superior, efficient and economical welds could be achieved using the welding conditions drawn from the statistical optimization.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] C. Dawes, “Laser Welding,” Abington Publishing, Abington, 1992.
[2] M. J. Donachie, “Titanium: A Technical Guide,” American Society for Microbiology International, Materials Park, 1988, pp. 133-156.
[3] G. E. P. Box and K. B. Wilson, “On the Experimental Attainment of Optimum Conditions,” Journal of the Royal Statistical Society, Series B, Vol. 13, No. 1, 1951, pp. 1-38.
[4] Z. Boumerzoug, C. Derfouf and T. Baudin, “Effect of Welding on Microstructure and Mechanical Properties of an Industrial Low Carbon Steel,” Engineering, Vol. 2, No. 7, 2010, pp. 502-506. doi:10.4236/eng.2010.27066
[5] B. I. Mendoza1, Z. C.Maldonado, H. A. Albiter and P. E. Robles, “Dissimilar Welding of Superduplex Stainless Steel/HSLA Steel for Offshore Applications Joined by GTAW,” Engineering, Vol. 2, No. 7, 2010, pp. 520-528. doi:10.4236/eng.2010.27069
[6] A. G. Olabi, K. Y. Benyounis and M. S. J. Hashmi, “Application of Response Surface Methodology in Describing the Residual Stress Distribution in CO2 Laser Welding of AISI304,” Strain: An International Journal Experimental Mechanics, Vol. 43, No. 1, 2007, pp. 37-46.
[7] G. Casalino, F. Curcio and F. M. C. Minutolo, “Investigation on Ti6Al4V Laser Welding Using Statistical and Taguchi Approaches,” Journal of Materials Processing Technology, Vol. 167, No. 2-3, 2005, pp. 422-428. doi:10.1016/j.jmatprotec.2005.05.031
[8] A. G. Olabi, G. Casalino, K. Y. Benyounis and M. S. J. Hashmi, “An ANN and Taguchi Algorithms Integrated Approach to the Optimization of CO2 Laser Welding,” Advances in Engineering Software, Vol. 37, No. 10, 2006, pp. 643-648. doi:10.1016/j.advengsoft.2006.02.002
[9] E. Akman, A. Demir, T. Canel and T. S?nmazcelik, “Laser Welding of Ti6Al4V Titanium Alloys,” Journal of Materials Processing Technology, Vol. 209, No. 8, 2009, pp. 3705-3713. doi:10.1016/j.jmatprotec.2008.08.026
[10] Z. Li, S. L. gobbi, I. Norris, S. Zolotosvkyand and K. H. Richter, “Laser Welding Technique for Titanium Alloy Sheet,” Journal of Material Process Technology, Vol. 65, No. 1-3, 1997, pp. 203-205. doi:10.1016/S0924-0136(96)02263-7
[11] E. M. Anawa and A. G. Olabi, “Using Taguchi Method to Optimize Welding Pool of Dissimilar Laser-Welded Components,” Optics & Laser Technology, Vol. 40, No. 2, March 2008, pp. 379-388. doi:10.1016/j.optlastec.2007.07.001
[12] D. C. Montgomery, “Design and Analysis of Experiments,” 2nd Edition, Wiley, New York, 2007.
[13] AI. Khuri and J. A. Cornell, “Response Surfaces Design and Analysis,” 2nd Edition, Marcel Dekker, New York, 1996.

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