Scholarly article on topic 'Parametric Optimization Of Gas Metal Arc Welding Process By Using Grey Based Taguchi Method On Aisi 409 Ferritic Stainless Steel'

Parametric Optimization Of Gas Metal Arc Welding Process By Using Grey Based Taguchi Method On Aisi 409 Ferritic Stainless Steel Academic research paper on "Materials engineering"

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Academic research paper on topic "Parametric Optimization Of Gas Metal Arc Welding Process By Using Grey Based Taguchi Method On Aisi 409 Ferritic Stainless Steel"

DE GRUYTER

TECHNOLOGICAL ENGINEERING volume XIII, number 1/2016 ISSN 2451 - 3156

DOI: 10.2478/teen-2016-0003

PARAMETRIC OPTIMIZATION OF GAS METAL ARC WELDING PROCESS BY USING GREY BASED TAGUCHI METHOD ON AISI 409 FERRITIC STAINLESS STEEL

1Nabendu Ghosh, 1Pradip Kumar Pal, 1Goutam Nandi

1Mechanical Engineering. Dept., Jadavpur University, Kolkata

Abstract

Welding input process parameters play a very significant role in determining the quality of the welded joint. Only by properly controlling every element of the process can product quality be controlled. For better quality of MIG welding of Ferritic stainless steel AISI 409, precise control of process parameters, parametric optimization of the process parameters, prediction and control of the desired responses ( quality indices) etc., continued and elaborate experiments, analysis and modeling are needed. A data of knowledge - base may thus be generated which may be utilized by the practicing engineers and technicians to produce good quality weld more precisely, reliably and predictively. In the present work, X-ray radiographic test has been conducted in order to detect surface and sub-surface defects of weld specimens made of Ferritic stainless steel. The quality of the weld has been evaluated in terms of yield strength, ultimate tensile strength and percentage of elongation of the welded specimens. The observed data have been interpreted, discussed and analyzed by considering ultimate tensile strength ,yield strength and percentage elongation combined with use of Grey-Taguchi methodology.

Keywords

AISI 409 Ferritic stainless steel, Gas metal arc welding, X-ray radiographic test, Grey-Taguchi methodology,

1. INTRODUCTION

Welding of ferritic stainless steel in general, and MIG welding of such steel in particular, can well be considered as one of the areas where more extensive research may contribute, in a significant way, to the precise control of welding procedure for better and acceptable quality of weldment. Researchers had done investigations on joining the 409 ferritic stainless steel materials with use of MIG welding technique, those are discussed below: M V Venkatesan, N Murugan, B M Prasad, A Manickavasagam [1] discussed the influence of flux cored arc welding (FCAW) process parameters such as welding current, travel speed, voltage and C02 shielding gas flow rate on bowing distortion of 409M ferritic stainless steel sheets of 2 mm in thickness. The bowing distortions of the welded plates were measured using a simple device called profile tracer. An experimental regression equation was developed to predict the bowing distortion and with this equation, it is easy to select optimized process parameters to achieve minimum bowing distortion. It is revealed that the FCAW process parameters have significant influence on bead profile and the bowing distortion. E. Taban, E. Deleu, A.

Article history:

Received 20.5.2016 Accepted 22.8.2016 Available online 10.10.2016

Dhooge, E. Kaluc [2] presented microstructural and toughness properties and mechanical properties of gas metal arc welded 6 mm thick modified X2CrNi12 stainless steel with two different heat inputs. According to results, grain size has dominant effect on impact toughness. Grain coarsening has no adverse influence either on tensile properties or on bend properties but the heat affected zone impact toughness for sub-zero temperatures generally decreases and this depends on the amount of grain coarsened microstructures and eventual precipitates present. P. Kanjilal, T.K. Pal, S.K. Majumdar [3] developed a rotatable designs based on statistical experiments for mixtures to predict the combined effect of flux mixture and welding parameters on submerged arc weld metal chemical composition and mechanical properties. Bead-on-plate weld deposits on low carbon steel plates were made at different flux composition and welding parameter combinations. The results show that flux mixture related variables based on individual flux ingredients and welding parameters have individual as well as interaction effects on responses, viz. weld metal chemical composition and mechanical properties. P.K. Palani, N. Murugan [4] investigated the effect of cladding parameters such as welding current, welding speed, and nozzle-to-plate distance on the weld bead geometry. The experiments were conducted for 317L flux cored stainless steel wire of size 1.2 mm diameter with IS:2062 structural steel as a base plate. Sensitivity analysis was performed to identify the process parameters exerting the most influence on the bead geometry and to know the parameters that must be most carefully controlled. Studies reveal that a change in process parameters affects the bead width, dilution, area of penetration, and coefficient of internal shape more strongly than it affects the penetration, reinforcement, and coefficient of external shape. M. Mukherjee and T.K. Pal [5] described The effect of heat input on martensite formation and impact properties of gas metal arc welded modified ferritic stainless steel (409M) sheets (as received) with thickness of 4 mm. The welded joints were prepared under three heat input conditions, i:e: 0.4, 0.5 and 0.6 kJ.mm-1 using two different austenitic filler wires (308L and 316L) and shielding gas composition of Ar + 5% CO2. The welded joints were evaluated by microstructure and charpy impact toughness. The dependence of weld metal microstructure on heat input and filler wires were determined by dilution calculation, Cr/Ni ratio, stacking fault energy (SFE), optical microscopy (OM) and transmission electron microscopy (TEM). It was observed that the microstructure as well as impact property of weld metal was significantly affected by the heat input

and filler wire. Weld metals prepared by high heat input exhibited higher amount of martensite laths and toughness compared with those prepared by medium and low heat inputs, which was true for both the filler wires. Furthermore, 308L weld metals in general provided higher amount of martensite laths and toughness than 316L weld metals. Hu et al [6] employed a high-precision magnetic sensor to detect the weld defects in aluminium friction stir welds. Lee et al. [7] have used the Taguchi method and regression analysis in order to optimize Nd-YAG laser welding parameters. Laser butt-welding of a thin plate of magnesium alloy using the Taguchi method has been optimized by Pan et al. [8] Ibrahim et al. [9] investigated the effects of robotic GMAW process parameters on welding penetration, hardness and microstructural properties of mild steel weldments of 6mm plate thickness. Murugan and Parmar [10] used a four-factors 5-levels factorial technique to predict the weld bead geometry (penetration, reinforcement, width and dilution %) in the deposition of 316L stainless steel onto structural steel IS2062 using the MIG welding process. Rosado et al [11] utilized the eddy currents probe to detect the imperfections in friction stir welds of aluminum. Senthil Kumar et al. [12] developed mathematical models by regression analysis to predict the effects of pulsed current tungsten inert gas welding parameters on tensile properties of medium strength AA 6061 aluminium alloy. Seshank et al. [13] used ANN and Taguchi method to analyze the effect of pulsed current GTAW process parameters on bead geometry of aluminium bead-on plate weldment. Sittichai et al. [14] investigated the effects of shielding gas mixture, welding current and welding speed on the ultimate tensile strength and percentage elongation of GMA Welded. Sourav Datta et al. [15] used Taguchi approach followed by grey relational analysis to solve multi -response optimization problem in submerged arc welding. Juang and Tarng [16] have adopted a modified Taguchi method to analyze the effect of each welding process parameter (arc gap, flow rate, welding current and speed) on the weld pool geometry (front and back height, front and back width) and then to determine the TIG welding process parameters combina-tion associated with the optimal weld pool geometry. Tarng and Yang [17] reported on the optimization of weld bead geometry in GTAW by using the Taguchi method. Tarng et al. [18] applied the modified Taguchi method to determine the process parameters for optimum weld pool geometry in TIG welding of stainless steel. Tarng et al. [19] also worked on the use of grey-based Taguchi method to determine optimum process parameters for submerged arc welding (SAW) in hard facing with consideration of multiple weld qualities. Yilmaz and Uzun [20] compared the results obtained from destruct-tive tests for mechanical properties of austenitic stainless steel. (AISI 304L and AISI 316L plates of 5 mm thickness) joints welded by GMAW and GTAW process. The joints were made by GMAW process using ER 316 L Si filler metal and by GTAW process using ER 308L and ER 316L filler metals. In the present work the effects of current, gas flow rate and nozzle to plate distance on ultimate tensile strength. Yield strength and percentage of elongation of butt-welded joints of austenitic stainless steel have been experimented and analyzed through grey base taguchi method.

2. EXPERIMENTAL PLAN, SET-UP AND PROCEDURE

has been used as design of experiment. Welding current, gas flow rate and nozzle to plate distance are selected as input parameters and three levels are considered for each of them. Welding process parameters and their levels are shown in Table 1. Welding Design Matrix as per L9 Taguchi Orthogonal Array Design is shown in Table 2. The Photographic view of welding set up is shown in Figure.1.

Figurel. Photographic view of welding set-up

Tablel.

Welding process parameters and their levels

UNIT NOTATION LEVELS

FACTORS 1 2 3

Welding Current A C 100 112 124

Gas Flow rate l.min-1 F 10 15 20

Nozzle to Plate Distance mm S 9 12 15

Table 2.

Welding Design Matrix as per L9 Taguchi Orthogonal Array Design of matrix

Welding Current (A) Gas flow rate(/.min-1) Nozzle to plate distance (mm)

Butt joints between ferritic stainless steel AISI 409 each of dimension 100mm x 65mm x 3mm are joined by MIG welding process by using austenitic filler wire AISI 316 L . No edge preparation is used as it is not recommended for welding of 3 mm thick austenitic stainless steel. Diameter of the electrode wire is selected 1.2 mm. Compositions of base material and the filler wire is given in Table3.

Table 3.

Compositions of base material and filler metal

ELEMENTS BASE METAL FILLER WIRE

409 316L

C 0.02 0.02

Mn 0.7S 1.85

Si 0.37 0.42

p 0.02 0.025

Cr 11.72 18.73

Ni 12.20

Mo 2.30

Cil - 0.19

AI - 0.01

5 0.02 0.01

T 0.48 -

In the present work, experiments are done in a planned experimental order Taguchi Orthogonal array design L9

Welding has been done on ESAB make AUTO K -400 MIG/MAG welding machine. Butt welded joints being done under varied input parameters, visual inspection and X-ray radiographic test of all welded specimens has been made. After visual inspections and X-ray radiographic test, tensile test specimens have been prepared from the welded joints, by cutting/machining. During cutting/machining of the tensile test specimens, small size cut pieces also been made. This small size - cut pieces have then been ground, polished and etched for studying microstructures. Results of Visual inspection and X-ray radiographic tests are reported in section 3. Result of microstructure is not reported in the present work, is expected to be communicated as a separate paper later. The tensile test specimens have been tested on tensile testing machine INSTRON as per ASTM standard. The major specifications of the tensile testing machine INSTRON are given below. Model No. : 5589

Serial No. : 95/1058

Maximum capacity : 600 KN

A schematic diagram showing the basic dimensions of the tensile test specimens of thickness 3mm is given in Figure. 2

Figure 2. Schematic diagram of the specimen prepared for tensile test

3. COPIES OF X-ray RADIOGRAPHIC FILM

X-ray radiographic test has been carried out for all nine samples. The copies of few radiographic films are shown in Figure3 -Figure4 respectively

Excess Weld Metal /

C112 GIO SI 2

4-09-409

Figure3. X-ray Radiographic Film For Sample No-11

A C112

& G IS

Figure 4. X-ray Radiographic Film For Sample No-14 4. TENSILE TEST RESULTS AND DISCUSSION

The tensile test specimens, prepared corresponding to L9 Taguchi Orthogonal Array design of experiments, have been tested for tensile strengths and the results obtained are given in table 4.

The Table 4 indicate that for many of the welded samples test results are satisfactory. The best result is obtained for the sample no.9 (Corresponding to current 124A, flow rate 20 l.min-1 and Nozzle to plate distance 12mm) For this sample, ultimate tensile strength = 453.6MPa and Yield strength = 321.9 MPa and percentage of elongation is 23.4The worst result in tensile testing has been obtained for the sample no. 5 (corresponding to current 112 A, gas flow rate 15l.min-1 and nozzle to plate distance 15 mm). For this sample yield strength 237 MPa and Ultimate tensile strength 368.3Mpa and percentage of elongation is 24.5

Table 4.

Tensile tests result as per L9 Taguchi Orthogonal Array Design of experiment

sample no. yield strength (MPa) ultimate strength (MPa) percentage of elongation (%)

1 296.5 431.6 25.5

2 255.1 376.5 15.05

3 256.0 377.3 18.53

4 289.8 416.6 17.6

5 237.0 368.3 24.5

6 288.0 424.0 17.0

7 296.5 431.6 25.5

8 286.5 423.5 18.0

9 321.9 453.6 23.4

5. OPTIMIZATION BY GREY BASED TAGUCHI METHOD 5.1 Taguchi method

Taguchi's method is developed by Dr. Genichi Taguchi, a Japanese quality management consultant. The quality engineering methods of Taguchi, employing design of experiments provide an efficient and systematic way to optimize designs for performance, quality and cost. It is one of the most important tools for designing high quality systems at reduced cost.Taguchi method is based on Orthogonal Array experiments, which provide much-reduced variance for the experiment, resulting optimal setting of process control parameters. Orthogonal Array provides a set of well-balanced experiments with less number of experimental runs. In order to evaluate the optimal parameter setting, Taguchi method uses a statistical measure of performance called signal-to-noise ratio that takes both the mean and the variability into account. The Taguchi method explores the concept of quadratic quality loss function. The S/N ratio is the ratio of the mean (signal) to the standard deviation (noise). The ratio depends on the quality characteristics of the product/process to be optimized. The standard S/N ratios generally used are Nominal-is-Best (NB), Lower-the-Better (LB) and Higher-the-Better (HB). The optimal setting is the parametric combination, which has the highest S/N ratio. However, traditional Taguchi method cannot directly solve multi-objective optimization problem. This can be achieved by grey based Taguchi method. The grey system theory proposed by Deng in 1982 has been proven to be useful for dealing with poor, incomplete, and uncertain information. The grey relational analysis based on grey system theory can be used to solve complicated interrelationships among multiple performance characteristics effectively.

Taguchi's S/N Ratio (n) for (NB) Nominal-the-Best

' n~i-1 a2

Taguchi's S/N Ratio (n) for (LB) Lower-the-Better

V = 10 lntoim-A

V = -10 Inuo-m-iVi

Taguchi's S/N Ratio(n) for (HB) Higher-the-Better

V = -10 lnio-^%1-2 /3/

5.2 Grey relational analysis

In grey relational analysis, experimental data i.e. measured features of quality characteristics of the product are first normalized ranging from zero to one. This process is known as grey relational generation. Next, based on normalized experimental data, grey relational coefficient is calculated to represent the correlation between the desired and actual experimental data. Then overall grey relational grade determined by averaging the grey relational coefficient corres-pondding to selected responses. The overall performance characteristic of the multiple response process depends on the calculated grey relational grade. This approach converts a multiple- response- process optimization problem into a single response optimization situation, with the objective function in overall grey relational grade. The optimal parametric combination is then evaluated by maximizing the overall grey relational grade.

In grey relational generation, the normalized data corresponding to Lower-the-Better (LB) criterion can be expressed as:

maxyi(k)-yi(k) maxyi(k)-minyi(k)

Xi(k) =

For Higher-the-Better (HB) criterion, the normalized data can be expressed as:

Xi (k) =

yi(k)-minyi(k)

maxyi(k)-minyi(k) /5/

Where Xi (k) is the value after the grey relational generation, min yi(k) is the smallest value of yi(k) for the kth response, and max yi(k) is the largest value of yi(k) for the kth response.

Normalizing of the experimental data according to Larger -the- Better (LB) by using the equation (5), Result of normalization of experimental data are shown in Table 6.

Where Xi (k) is the value after the grey relational generation, min yi(k) is the smallest value of yi(k) for the kth response, and max yi(k) is the largest value of yi(k) for the kth response.

Normalizing of the experimental data according to Larger -the- Better (LB) by using the equation (5), Result of normalization of experimental data are shown in Table 5.

Table5.

Normalizing of the experimental data

Sample No. Normalized Values

Yield Strength (MPa) Ultimate Tensile Strength (MPa) Percentage of Elongation (%)

Ideal 1.000 1.000 1.000

1 0.70082 0.74209 1.00000

2 0.21319 0.09613 0.00000

3 0.22379 0.10551 0.33301

4 0.62191 0.56624 0.24402

5 0.00000 0.00000 0.90431

6 0.60071 0.65299 0.18660

7 0.70082 0.74209 1.00000

8 0.58304 0.64713 0.28230

9 1.00000 1.00000 0.79904

Next, the grey relation coefficient ^i(k) can be calculated as

r çk) = /6/

Aoi(k)+ 8Amax

After averaging the grey relation coefficients, the grey relational grade Yi can be computed as:

Where n = number of process responses. The higher value of grey relational grade corresponds to intense relational degree between the reference sequence xo(k) and the given sequence Xi(k). The reference sequence xo(k) represents the best process sequence. Therefore, higher grey relational grade means that the corresponding parametric combination is closer to the optimal.

Table 6.

The grey relation coefficient §(k)

Sample. No. Grey relational coefficients ^(k)

Yield Strength (MPa) Ultimate Tensile Strength (MPa) Percentage of Elongation (%)

1 0.41638 0.41164 0.40255

2 0.70107 1.00000 0.83874

3 0.69081 0.97967 0.82575

4 0.44567 0.49015 0.46894

5 1.00000 1.00000 1.00000

6 0.45425 0.44800 0.43366

7 0.41638 0.41164 0.40255

8 0.46166 0.45061 0.43587

9 0.33333 0.33333 0.33333

Overall grey relation grade is shown in Table 7.

Table 7.

Overall grey relation grade

Sample No. Grey Relational Grade

1 0.41019

2 0.84660

3 0.83208

4 0.46825

5 1.00000

6 0.44530

7 0.41019

8 0.44938

9 0.33333

Main effect plot for means is shown in Figure7. Analysis of Variance for overall grey relation grade is shown in Table 8

Main effect plot for means is shown in Figure 5.

Figure 5. Main effect plot for means

With the help of mean main effect plots and S/N ratio plots, optimum parametric combination has been determined. The optimal factor setting becomes C1F2S3 (i.e. welding current=100A, Gas flow rate = 15l/min and Nozzle to plate distance =15mm).

S/N ra tio plot is shown in Figure6

Figure 6. S/N ratio plot

From table 8, it is found that gas flow rate and nozzle to plate distance are most significant factors than welding current

Table 8.

Analysis of Variance for overall grey relation grade

DF Seq SS AdJ SS AdJ MS F P

CURRENT 2 0.1630 0.1630 0.0815 55.1 0.018

GAS FLOW RATE 2 0.2121 0.2121 0.1060 71.80 0.014

NOZZLE TO PLATE DISTANCE 2 0.1824 0.1824 0.0912 61.73 0.016

ERROR 2 0.0029 0.0029 0.0014

TOTAL 8 0.5605

R-Sq = 99.47 % R-Sq(adj) = 97.89%

Table 9

Confirmatory results

Obtained optimum parametric condition by Taguchi method Obtained ultimate Tensile strength (U.T.S) by confirmatory test

Current (C ) 100 A U.T.S= 454MPa

Glass flow rate (F) 15 /.min-1

Nozzle to plate distance (S) 15mm

Confirmatory test is conducted at optimal parameter combination (C1F2S3) to check the validity of the optimum welding condition. From the results of confirmatory test, it is found that optimum welding parametric condition produced maximum UTS, this value shows the validation of the proposed optimization methodology

6. CONCLUSIONS

• Results of X-ray radiography test indicate: lack of penetration, low - level porosity, lack of fusion, weld depressions and surface mark in some of the samples.

• Results of visual inspection and X-ray radio-graphic tests are compared, some consistency are founds.

• The best result is obtained for the sample no.9 (Corresponding to current 124A, flow rate 20 l.min-1 and Nozzle to plate distance 12mm) For this sample, ultimate tensile strength = 453.6MPa and Yield strength= 321.9 MPa and percentage of elongation is 23.4The worst result in tensile testing has been obtained for the sample no. 5(corresponding to current 112 A, gas flow rate 15 l.min-1 and nozzle to plate distance 15 mm). For this sample yield strength 237Mpa and Ultimate tensile strength 368.3Mpa and percentage of elongation is 24.5

• With the help of mean main effect plots and S/N ratio plots, optimum parametric combination has

been determined. The optimal factor setting becomes C1F2S3 (i.e. welding current=100A, Gas flow rate = 15 l.min-1 and Nozzle to plate distance =15mm)

7. REFERENCES

[1] M V Venkatesan, N Murugan, B M Prasad, A Manic-kavasagam,Influence of FCA Wel- ding Process Parameters on Distortion of 409M Stainless Steel for Rail Coach Buil-ding, Journal of Iron And Steel Research, International. 2013, 20(1): 71-78

[2] E. Taban, E. Deleu, A. Dhooge, E. Kaluc, " Gas metal arc welding of modified X2CrNi12 ferritic stainless steel"' Kovove Mater. 45 2007 67-74

[3] P. Kanjilal, T.K. Pal, S.K. Majumdar, " Com-bined effect of flux and welding parame-ters on chemical composition and mecha-nical properties of submerged arc weld metal", Journal of Materials Processing Technology 171 (2006) 223-231.

[4] P.K. Palani, N. Murugan, "Sensitivity Analysis for Process Parameters in Cladding of Stainless Steel by Flux Cored Arc Welding", Journal of Manufacturing Processes Vol. 8/No. 2,2006

[5] M. Mukherjee and T.K. Pal, " Influence of Heat Input on Martensite Formation and Impact Property of Ferritic-Austenitic Dissimilar Weld Metals", J. Mater. Sci. Technol., 2012, 28(4), 343{352.

[6] B, Hu., R, Yu., and H, Zou., Magnetic nondestructive testing methods for thin - plates aluminium alloys. NDT&E Interna-tional 47: 66-69 (2012)

[7] H. K., Lee, H. S., Han, K. J., Son, and S. B. Hong., Optimization of Nd-YAG laser welding parameters for sealing small titanium tube ends. J. of Materials Science and Engineering, Vol. A415, pp.149-155 (2006)

[8] L. K., Pan, C. C., Wang, Y. C., Hsiso and K. C., Ho, Optimization of Nd-YAG laser wel-ding onto magnesium alloy via Taguchi analysis. J. of Optics & Laser Technology, Vol. 37, pp. 33-42 (2004)

[9] Ibrahim, I. Z., et al., The effect of Gas Metal Arc Welding (GMAW) processes on different welding parameters. Procedia Engineering 41:1502-1506 (2012)

[10] N. Murugan and R. S. Parmar., effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel. J.of Materials Processing Technology, Vol. 41, pp. 381-398 (1994)

[11] Rosado et al., Advanced technique for nondestructive testing of friction stir welding of metals. Journal of Measurement 43: 1021-1030 (2010)

[12] Senthil Kumar, T., et al., Influences of pul-sed current tungsten inert gas welding on the tensile properties of AA 6061 aluminium alloy. Materials and Design 28: 2080-2092 (2007)

[13] Seshank, K., et al., Prediction of bead geometry in pulsed current gas tungsten arc welding of aluminium using artificial neural networks. Proceedings of international conference on information and knowledge engineering, IKE; June 23-26, Las Vegas [NV], USA: 149-53 (2003)

[14] Sittichai, K., Santirat, N., and Sompong, P., A study of gas metal arc welding affecting mechanical properties of austenitic stainless steel AISI 304. World Academy of Science, Engineering and Technology: 61:402-405 (2012)

[15] Datta S, Bandyopadhyay A, Pal P K, Grey-based taguchi method for optimization of bead geometry in submerged arc bead -on-plate welding. Int J Adv Manuf Technol 39:1136-1143 (2008)

[16] S. C. Juang and Y. S. Tarng. , Process para-meters selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel. J. of Materials Processing Technology, Vol. 122, 2002, 33-37 (2002)

[17] Tarng YS, Yang WH., Optimization of the weld bead geometry in gas tungsten arc welding by the Tagu-chi method. Int J Adv Manuf Technol 14: 549-554 (1998)

[18] Tarng YS, Yang WH, Juang SC, The use of fuzzy logic in the Taguchi method for the optimisation of the submerged arc welding process. Inter J Adv Manuf Technol 16: 688-694 (2000)

[19] Tarng YS, Juang SC, Chang CH, The use of grey-based Taguchi methods to determine submerged arc welding process parame-ters in hard facing. J Mater Process Technol 128 (1-3):1-6 (2002)

[20] Yilmaz, R.,and Uzun, H., Mechanical pro-perties of austenitic stainless steels welded by GMAW and GTAW. Journal of Marmara for Pure and Applied Sciences 18: pp. 97-113 (2002)