1. Introduction

Laser cutting is one of the important applications of lasers in industry, especially for machining materials that are difficult to cut. Compared with other conventional machining processes, laser cutting removes little material, involves highly localized heat input to the work piece, minimizes distortion, and offers no tool wear [1]. Particular interests of manufacturers using laser cutting are the productivity and the quality of components made by the laser cutting process. Both aspects are managed by selection of appropriate laser process parameters, which are unique for each material and thickness. Most work reviewed in the literature considers only one or two characteristic properties of the laser cut surface to describe quality [2]. Kerf width, surface roughness and size of heat affected zone are often used to describe laser cut quality. Some researchers [3] show that the size of heat affected zone increases with increase of the laser power and decreases with increase of the cutting speed and gas pressure. Oxygen gas as assist gas produces better surface roughness compared to air and nitrogen during laser cutting of tungsten composite materials using pulsed Nd: YAG [4]. They also observe that the nitrogen assist gas develops an oxide free surface and a low discoloration, while the oxygen assist gas surface is strongly oxidized and discoloured. The effect of laser power, cutting speed and oxygen assist gas pressure on the cut quality in laser cutting of tungsten alloy is analysed in [5]. They define optimal cutting parameters in laser cutting examined alloy by using oxygen as assist gas.

There are various aspects of laser beam machining that can be modelled with different methods in order to predict quality characteristics which are essential for any manufacturing process [6]. In [7] technique to predict surface roughness in the laser cutting process is developed for the first time by analysing the dynamic phenomenon that happens within the cutting front. The quality characteristics (such as kerf width, surface roughness and cut edge slope) were observed for the various cutting parameters such as laser power, cutting speed and assist gas pressure during pulsed C[O.sub.2] laser cutting of Al6061/SiCp/[Al.sub.2][O.sub.3] composite [8]. Hybrid approach of grey based response surface methodology for predicting the optimal combination of laser cutting parameters is used. A grey relational analysis is used to determine a single optimized set of cutting parameters in precision laser cutting of three different thermoplastics [9]. It is found that the laser power has dominant effect on heat affected zone for all thermoplastics. The relation between process parameters and quality characteristics in C[O.sub.2] laser cutting of tungsten alloy was modelled with artificial neural network [10]. It is shown that the proposed model could be useful tool for surface roughness and kerf width prediction. In [11] comparison of surface roughness during C[O.sub.2] laser cutting of tungsten alloy plate using oxygen as assist gas, based on control charts made by statistical process control (SPC) approach is reported. For further analysis, it is especially interesting to analyse uncommon materials and alloys where the common knowledge is not applicable [12].

Comprehensive review of the literature shows that research work in the area of laser cutting of tungsten alloy is limited. Hence, this paper aims to investigate the effect of laser cutting process parameters on the cut quality features and then to obtain the optimal cutting conditions by using Taguchi design of experiments through the study of signal to noise ratio that would lead to the desired quality features. The process parameters such as assist gas type (oxygen, nitrogen and air) and assist gas pressure are considered as the experimental variables. The experimental work is carried out on tungsten alloy sheet with thickness of 1 mm in C[O.sub.2] laser cutting process.

2. Experimental procedure

The experiments are carried out on a Rofin C[O.sub.2] laser system with a nominal output power of 2000 W in CW mode at a wavelength of 10.6 [micro]m with a high quality beam, as shown in figure 1. The experimental investigations are conducted at the University of Applied Science Jena in Germany. Tungsten alloy sheet (92.5% pure tungsten in a matrix of nickel and iron) with thickness of 1 mm is used for this experiment. The laser beam is focused using a 127 mm focal length lens. Assist gases are used coaxially with the laser beam via a 2 mm exit diameter nozzle. Focus position, nozzle stand of distance (stand-off), laser power and cutting speed are kept constant throughout the main experimentation. The constant process parameters are shown in table 1.

Assist gas type and pressure are selected as variable process parameters. Process parameters with their units and values at different levels are listed in table 2. Testing the effect of one parameter on the cut quality requires the variation of one parameter while keeping the other parameters at the pre-selected values.

Observed quality characteristics are top kerf width, heat affected zone (HAZ), surface roughness and dross formed at bottom side. Visual inspections of each cut are carried out to ensure that no pitting and burrs are present within the cut areas. Measurements and sample geometry are shown in figure 2.

Surface roughness of the cut edge is measured in terms of the average roughness Ra, using a Taylor-Hobson stylus instrument. Roughness is measured along the length of a cut approximately in the middle of the thickness. Five consistent surface roughness values of each sample are measured and an average value is calculated for each sample.

The top kerf width and size of heat affected zone are measured using Stemi optical microscope fitted with a video camera and a zoom lens. Kerf width and heat affected zone are measured in the microscopic images of each sample at five different places and an average value is calculated for each sample and each controlled parameters. The dross formed at bottom side is observed using a TesaVisio microscope.

3. Experiment design

Taguchi method is used widely in engineering analysis to optimize performance characteristics by the means of settings of design parameters [13]. Taguchi methodology for robust parameter design is an off-line statistical quality control technique in which the level of controllable factors or input process parameters are chosen in a way to nullify the variation in responses due to uncontrollable factors such as humidity, vibration, noise etc. The objective of Taguchi approach is to determine the optimum setting of process parameters or control factors, thereby making the process insensitive to the sources of variations due to uncontrollable factors [14]. Taguchi has tabulated 18 basic types of designed experimental matrixes known as orthogonal arrays. The selection of orthogonal arrays is based on the number of controllable factors and their levels and interactions [15]. In this method main process parameters or control factors which influence process results are set as input parameters and the experiment is performed per specifically designed orthogonal array. More than one test per trail can be used to conduct the experiments. It increases the sensitivity of experiments to detect small changes in averages of responses. Economic consideration too can be performed for conducting the repeated experiments with the same experimental run.

In this study [L.sub.9] Taguchi orthogonal array design is selected. Measured experimental values obtained for different quality characteristics in each experimental run are given in table 3.

Taguchi method uses the S/N ratio to measure characteristic deviating from the desired value. The S/N ratio is the ratio of the mean to the standard deviation. Taguchi method suggests that the signal to noise ratio (S/N) can be used as a quantitative analysis tool. Since smaller kerf width, heat affected zone and surface roughness values are desired in this experiment the signal-to-noise ratio is chosen as:

S/N = -10 log([1/n][[summation].sup.n.sub.i=1][y.sup.2.sub.i]) (1)

where: [y.sub.i] is the observed data of quality characteristic at the i-th trial and n is the number of repetitions at the same trial. The S/N ratio represents the desired part/ undesired part and the aim is always to maximize the S/N ratio whatever nature of quality characteristics are. Based on the experimental results S/N ratio for each controlled parameters is obtained as shown in table 4.

4. Results and discussion

4.1. The effect process parameters on top kerf width

The analysis of the experimental results revealed the assist gas type as the most important parameter determining the kerf width (see figure 3). The kerf width slightly changes as the assist gas pressure increases.

From S/N ratio analysis shown in figure 4 optimal process parameters for kerf width are determined as assist gas pressure 12.5 bar and nitrogen as assist gas. Also it can be observed form figure 5 that kerf width slightly increases as assist gas pressure increases. Narrowest kerf width is obtained in laser cutting when used nitrogen as assist gas for all of three levels assist gas pressure. While the largest kerf width is made during C[O.sub.2] laser cutting when used oxygen as assist gas.

4.2. The effect process parameters on heat affected zone

Measured experimental values obtained for the size of heat affected zone are shown in figure 6. It can be observed that the largest heat affected zone is obtained in C[O.sub.2] laser cutting when used air as assist gas. The reason for this may be that the lowest cutting speed is selected when air is used as an assist gas in comparison to two other assist gasses that are used in the experiments.

The effect of process parameters on the size of heat affected zone for the S/N ratio and for the means are shown in figures 7 and 8, respectively. From the S/N ratio analysis shown in figure 7 optimal process parameters for the heat affected zone are determined as assist gas pressure 15 bar and nitrogen as assist gas. From figure 8 it can be observed that the size of heat affected zone decreases as the assist gas pressure increases. Smallest size of heat affected zone is obtained when nitrogen as assist gas is used.

4.3. The effect process parameters on surface roughness

The measured experimental values obtained for surface roughness are shown in figure 9. It can be observed that the smallest surface roughness is obtained when nitrogen as assist gas is used. It can be seen that surface roughness increases as the assist gas pressure increases for all of three assist gasses which are used in the experiments. On the microscopic images shown in figure 9 it can be seen that the dross is formed on the bottom side of sample along the entire length of a cut at nitrogen pressure of 10 bar, while this is not the case at a nitrogen pressure of 12.5 bar.

From the S/N ratio analysis shown in figure 10 optimal process parameters for surface roughness are determined as assist gas pressure 10 bar and nitrogen as assist gas. Also it can be observed form figure 11 that surface roughness slightly increases as assist gas pressure increases over 12.5 bar. It can be concluded that the measured experimental values of surface roughness which are obtained for all of three levels assist gas pressure belonged to the same class of roughness.

5. Conclusion

In this paper the effect of the process parameters on the quality characteristics such as top kerf width, heat affected zone and surface roughness in C[O.sub.2] laser cutting of tungsten alloy with thickness of 1 mm is analysed using Taguchi orthogonal arrays method. Type of assist gas has the most significant effect on the cut quality in C[O.sub.2] laser cutting process.

Medium level of assist gas pressure and nitrogen as assist gas as the optimal process parameters for desired cut quality in C[O.sub.2] laser cutting of tungsten alloy are recommended.

Future work should include application of Taguchi orthogonal arrays method in laser cutting process, including the effect of different process parameters such as laser power, cutting speed, focus position and other parameters.

DOI: 10.2507/27th.daaam.proceedings.024

5. Acknowledgments

The authors would like to thank the Federal Ministry of Education and Science, Bosnia and Herzegovina for financial support of this study through the project entitled: "Non-conventional machining processes: laser and plasma cutting". Also, thanks to the Department of Laser and Opto-Technologies at the University of Applied Science Jena, Germany where experimental work was performed.

6. References

[1] Ready, J.F. (1997). Industrial Applications of Lasers, Academic Press, ISBN: 978-0-12-583961-7, United States of America.

[2] Avanish, K.D. & Vinod, Y. (2008). Laser beam machining--A review, International Journal of Machine Tools & Manufacture, Vol. 48, Issue 6, (May 2008) pp. 609-628, ISSN: 0890-6955.

[3] Hanadi, G.S.; Mohy, S.M.; Yehya, B. & Wafaa, A.A. (2008). CW Nd:YAG laser cutting of ultra-low carbon steel thin sheets using [O.sub.2] assist gas, Journal of Materials Processing Technology, Vol. 196, (January 2008) pp. 64-72, ISSN: 0924-0136.

[4] Uebel, M.; Buerger, W.; Schoele, H.; Stoerzner, F.; Meudtner, A. & Stibritz, G. (2008). Requirements to precision laser cutting processing of refractory metals, Annals of DAAAM for 2008 & Proceedings of the 19th International DAAAM Symposium, 22-25th October 2008, Trnava, ISSN 1726-9679, ISBN 978-3- 901509-68-1, Katalinic, B. (Ed.), pp. 1419-1420, Published by DAAAM International, Vienna.

[5] Begic, D.; Kulenovic, M.; Cekic, A. & Bliedtner, J. (2009). CW C[O.sub.2] laser cutting of tungsten alloy using [O.sub.2] assist gas, Annals of DAAAM for 2009 & Proceedings of the 20th International DAAAM Symposium, 25-28th November 2009, Vienna, ISSN 1726-9679, ISBN 978-3-901509-70-4, Katalinic, B. (Ed.), pp. 1345-1347, Published by DAAAM International, Vienna.

[6] Schaaf, P. (2010). Laser processing of materials, Springer Series in Materials Science, 139, Springer-Verlag, ISBN: 978-3-642-13280-3, Berlin.

[7] Pietro, P.D. & Yao Y. (1995). A new technique to characterize and predict laser cut striations, International Journal of Machine Tools and Manufacture, Vol. 35, Issue 7, (July 1995) pp. 993-1002, ISSN: 0890-6955.

[8] Adalarasan, R.; Santhanakumar, M. & Rajmohan, M. (2015). Optimization of laser cutting parameters for Al6061/SiCp/Al2O3 composite using grey based response surface methodology (GRSM), Measurement, Vol. 73, (September 2015) pp. 596-606, ISSN: 0263-2241.

[9] Tamrin, K.F.; Nukman, Y.; Choudhury, I.A. & Shirley, S. (2015). Multiple- objective optimization in precision laser cutting of different thermoplastics, Optics and Lasers in Engineering, Vol. 67, (April 2015) pp. 57-65, ISSN: 0143-8166.

[10] Klancnik, S.; Begic-Hajdarevic, D.; Paulic, M.; Ficko, M.; Cekic, A. & Cohodar Husic, M. (2015). Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method, Strojniski vestnik-Journal of Mechanical Engineering, Vol. 61, No. 12, (December 2015) pp. 714-720, ISSN: 0039-2480.

[11] Begic-Hajdarevic, D.; Pasic, M.; Vucijak, B. & Cekic, A. (2016). Statistical Process Control of Surface Roughness during C[O.sub.2] Laser Cutting using Oxygen as Assist Gas, Proceedings of the 26th DAAAM International Symposium, 21st-24th October 2015, Zadar, ISSN 1726-9679, ISBN 978-3-902734-07-5, Katalinic, B. (Ed.), pp. 0247-0255, Published by DAAAM International, Vienna, DOI:10.2507/26th.daaam.proceedings.034.

[12] El-Labban, H.F.; Mahmoud, E.R.I. & Al-Wadai, H. (2014). Laser cladding of Ti-6Al-4V alloy with vanadium carbide particles, Advances in Production Engineering & Management, Vol. 9, No. 4, (December 2014) pp. 159-167, ISSN: 1854-6250.

[13] Taguchi, G. (1962). Tables of orthogonal arrays and linear graphs, Maruzen, Tokyo.

[14] Phadke, M.S. (1989). Quality engineering using robust design, Prentice- Hall, ISBN-10: 0137451679, New Jersey.

[15] Ross, P.J. (1988). Taguchi techniques for quality engineering, McGraw Hill, ISBN-10: 0070539588, New York.

This Publication has to be referred as: Begic-Hajdarevic, D[erzija]; Pasic, M[ugdim]; Cekic, A[hmet] & Mehmedovic, M[uhamed] (2016). Optimization of Process Parameters for Cut Quality in C[O.sub.2] Laser Cutting Using Taguchi Method, Proceedings of the 27th DAAAM International Symposium, pp.0157-0164, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-08-2, ISSN 1726-9679, Vienna, Austria

Caption: Fig. 1. C[O.sub.2] laser system

Caption: Fig. 2. The measured characteristics of cut quality

Caption: Fig. 3. Experimental observation for kerf width

Caption: Fig. 4. The effect of process parameters on kerf width for S/N ratio

Caption: Fig. 5. The effect of process parameters on kerf width for the means

Caption: Fig. 6. Experimental observation for heat affected zone

Caption: Fig. 7. The effect of process parameters on heat affected zone for S/N ratio

Caption: Fig. 8. The effect of process parameters on heat affected zone for the means

Caption: Fig. 9. Experimental observation for surface roughness

Caption: Fig. 10. The effect of process parameters on surface roughness for S/N ratio

Caption: Fig. 11. The effect of process parameters on surface roughness for the means

Table 1. Constant process parameters and their values

 Assist      Laser power    Cutting speed    Stand-off
 gas type

  Oxygen        2000 W       4000 mm/min      1.00 mm
 Nitrogen       2000 W       1750 mm/min      0.75 mm
   Air          2000 W       1500 mm/min      1.00 mm

 Assist      Focus position
 gas type

  Oxygen        -0.50 mm
 Nitrogen       -0.50 mm
   Air          -1.00 mm

Table 2. Process parameters and their different levels

      Parameter         Unit   Level 1    Level 2     Level 3

   Assist gas type      ---     Oxygen      Air      Nitrogen
 Assist gas pressure    bar       10       12.50        15

Table 3. Kerf width, heat affected zone and surface
roughness obtained from experiments

Exp. no    Process parameter level

            Gas Type        Gas
                          Pressure

   1            1            1
   2            1            2
   3            1            3
   4            2            1
   5            2            2
   6            2            3
   7            3            1
   8            3            2
   9            3            3

Exp. no           Measured parameters

             Kerf       HAZ, mm      Ra,
           width, mm               [micro]m

   1         0.282       1.386       5.805
   2         0.306       1.322       7.532
   3         0.310       1.326       7.303
   4         0.240       1.477       6.150
   5         0.244       1.395       7.026
   6         0.247       1.357       7.073
   7         0.200       1.457       5.167
   8         0.174       1.322       5.917
   9         0.175       1.196       6.215

Table 4. S/N ratio calculated for kerf width, heat affected
zone and surface roughness

Exp.   Process parameter level              S/N ratio
 no
       Gas Type       Gas        Kerf Width     HAZ      Ra
                    Pressure

 1         1            1           10.99      -2.83   -15.27
 2         1            2           10.28      -2.42   -17.54
 3         1            3           10.17      -2.45   -17.27
 4         2            1           12.39      -3.39   -15.78
 5         2            2           12.25      -2.89   -16.93
 6         2            3           12.14      -2.65   -16.99
 7         3            1           13.98      -3.27   -14.26
 8         3            2           15.19      -2.42   -15.44
 9         3            3           15.14      -1.55   -15.87