This paper examines the influence of cutting parameters, namely cutting speed, feed and depth of cut onto surface roughness after DSS turning process. The study included developing a mathematical model to determine the surface roughness. Verification research has been carried out on CNC lathe; hence the test plan has been adjusted to the possibility of programmable machines controlling GE Fanuc Series 0-T. The comparison of results obtained by given experimental plan was performed in industrial company

G. Krolczyk

M. Gajek

S. Legutko

Hrčak - Portal of scientific journals of Croatia

259METALURGIJA 52 (2013) 2, 259-262G. KROLCZYK, S. LEGUTKO, M. GAJEKPREDICTING THE SURFACE ROUGHNESS IN THE DRY MACHINING OF DUPLEX STAINLESS STEEL (DSS)Received – Prispjelo: 2012-06-17Accepted – Prihvaćeno: 2012-10-25Preliminary note – Prethodno priopćenjeISSN 0543-5846METABK 52(2) 259-262 (2013)UDC – UDK 669.15:621.773:621.9.015=111G. Krolczyk, M. Gajek, Opole University of Technology, Department of Technology and Work Environment Engineering, Opole, Poland, S. Le-gutko, Poznan University of Technology, Institute of Mechanical Tech-nology, Poznan, PolandThis paper examines the infl uence of cutting parameters, namely cutting speed, feed and depth of cut onto surface roughness after DSS turning process. The study included developing a mathematical model t o determine the sur-face roughness. Verifi cation research has been carried out on CNC lathe; henc e the test plan has been adjust ed to the possibility of programmable machines controlling GE Fanuc Series 0-T. The comparison of results obtained by given experimental plan was performed in industrial company.Key words: Duplex Stainless Steel, dry machining, surface roughness, response surface method (RSM)INTRODUCTIONDuplex stainless steel is gaining importance accord-ing to companies producing construction materials. It is reﬂ ected in the wide range of these products available in the market. According Olszak [1] DSS is classiﬁ ed as almost unworkable. The wear process of tool wedges, which is largely dependent on cutting parameters, is an important factor. Investigation performed by Alauddin et al. [2] has revealed that when the cutting speed is in-creased, productivity can be maximised and surface quality can be improved. The wear of the cutting tool wedge leads to a deterioration in quality of the machined surface, and in the most commonly used surface rough-ness parameter in production which is the arithmetic average deviation from the average line proﬁ le. Surface ﬁ nish can be characterised by various parameters and mostly such as surface roughness Ra [3]. The basic re-quirement in the application of indexable tool inserts in industrial conditions is the total increase in production; not the precision performance of its particular machine parts. According to Smith [4], where the equipment stocks are consolidated and the materials used in cutting tools are more universal, we can, in industrial condi-tions, use a smaller number of types and geometry of the cutting tool. Smaller stocks of indexable tool inserts allow us to more effectively optimize the production process. The above-mentioned aspects, combined with the optimization of the cutting speed, feed and depth of cut, allow the desired production targets to be met. Due to an optimization of the cutting parameters, it is possi-ble to take full advantage of the basic equipment; as a result you can expect a large increase in overall produc-tion efﬁ ciency. In order to know surface quality and di-mensional properties is necessary to employ theoretical models making it feasible to do predictions in function Table 3 Coded indication of the study planTestNo.Coded factors Decoded real valuex1 x2 x3 vC f ap 1 -1 -1 -1 70 0,24 1,42 -1 -1 +1 70 0,24 2,63 -1 +1 -1 70 0,36 1,44 -1 +1 +1 70 0,36 2,65 +1 -1 -1 130 0,24 1,46 +1 -1 +1 130 0,24 2,67 +1 +1 -1 130 0,36 1,48 +1 +1 +1 130 0,36 2,69 -1,682 0 0 50 0,3 210 1,682 0 0 150 0,3 211 0 -1,682 0 100 0,2 212 0 1,682 0 100 0,4 213 0 0 -1,682 100 0,3 114 0 0 1,682 100 0,3 315 0 0 0 100 0,3 216 0 0 0 100 0,3 217 0 0 0 100 0,3 218 0 0 0 100 0,3 219 0 0 0 100 0,3 220 0 0 0 100 0,3 2Table 1  Chemical composition of 1.4462 duplex stainless steel /wt %Cmax Simax Mnmax Pmax Smax Cr Ni Mo N0,03 1 2 0,03 0,02 21 - 23 4,5 - 6,5 2,5 - 3,5 0,1 -0,22Table 2 Cutting tool specifi cationTool Substrate OthersT1 Hardness: 1 350 HV3Grade: M25, P35Coatings: Ti(C,N) - (2 µm) (top layer)Al2O3 - (1,5 µm) (middle layer)TiN - (2 µm) (bottom layer)Coating technique: CVD260  METALURGIJA 52 (2013) 2, 259-262G. KROLCZYK et al.: PREDICTING THE SURFACE ROUGHNESS IN THE DRY MACHINING…of operation conditions. The response surface method (RSM) is practical, economical and relatively easy for use [5]. The experimental data was utilized to build mathematical model for second-order model. This method has been used by some researchers for determi-nation of tool life and surface roughness [3, 6–10]. Ta-raman [6], Hasegawa et al. [3], Sundaram and Lambert [7] used the RSM for predicting surface roughness. Baradie [8], Mital and Mehta [9], Kopac et al. [10] in-vestigated the use of RSM in developing a surface roughness prediction model.EXPERIMENTAL TECHNIQUESWorkpiece and cutting tool materialsMachined material was 1.4462 (DIN EN 10088-1) steel with a ferritic-austenitic structure containing about 50 % of austenite. The ultimate tensile strength Rm = 700 MPa, Brinell hardness - 293 HB. The elemental composition of the machined material and technical de-tails of the cutting tools are given in Tables 1 and 2 re-spectively. Cutting tool inserts of TNMG 160408 desig-nation clamped in the tool shank of ISO-MTGNL 2020-16 type were employed. Based on the industry recom-mendations a range of cutting parameters T1: vc = 50 - 150 m/min, f = 0,2 - 4 mm/rev, ap = 1 - 3 mm was se-lected. The study was conducted within a production facility. The research program was carried out on a lathe CNC 400 Famot – Pleszew.Research planThe static determined selective-multivariate uniform static - rotatable PS/DS-P:λ program as the method of optimization for DSS cutting parameters has been se-lected [11]. A choice of the PS/DS-P:λ program was dictated with the assumption that the second-degree polynomial function model will be a nonlinear model which can be reduced to a linear model. The second-degree polynomial function has been chosen because there are no restrictions in research related to the meas-urement technique. The required number of experimen-tal points is N = 23 + 6 + 6 = 20 (Table 3). There are eight factorial experiments (3 factors on two levels, 23) with added 6 star points and centre point (average level) re-peated 6 times to calculate the pure error [12].MODELING OF SURFACE ROUGHNESS AND ITS VERIFICATIONThe aim of this study was an attempt to verify, in industrial conditions, the calculated value of the func-tion describing surface roughness after turning DSS. The study was conducted in industrial conditions. Cho-sen results are presented in Table 4.Surface roughness modelBased on the PS/DS-P:λ program and the experi-mental data the polynomial function of surface rough-ness has been elaborated:Ra =  – 1,7163 – 0,013259·vC + 27,5189·f – –1,4311·ap – 0,000056162·vC2 – –15,9793·f2 + 0,11521·ap2 + +0,047667 vC ·f + 0,0046265 vC ·ap + +1,4583 f·ap.    (1)Verifi cation study of modelThe evaluation of the mathematical model was per-formed using the Student’s t test to compare two mean values of populations with normal distributions and ho-mogeneous variances. Statistical calculations were per-formed with the Statistica 9.0 program [13].The assumptions of normality were examined using the Shapiro - Wilk test for the model of surface rough-ness (Table 5).Table 4  Verifi ed research parameters of the model of surface roughnessCutting parameters Average value of research Ra /µmCalculation resultsRa /µmvC / m/min f /mm/rev aP /mm150 0,2 2 1,92 0,90150 0,4 2 6,49 6,5050 0,2 2 1,65 1,4750 0,4 2 5,89 6,11Table 5  Tests of normality for the model of surface roughnessVariable n W pAverage value of research 4 0,8100 0,1215Calculation results 4 0,8070 0,1154Table 6  The results of F-statistic model calculation for surface roughnessF 1,3482p 0,8118As the level of signiﬁ cance of p is greater than 0,05 for the test case, there is no reason to reject the hypoth-esis of normal distribution.Homogeneity of variances was checked by Fisher test. Two general populations has been examined with normal distributions N (m1, σ1) and N (m2, σ2), where the parameters of these distributions are unknown. There were two sample sizes n1 = 4 and n2 = 4. On the basis of test results the hypothesis was tested H0: σ12 = σ22, against the alternative hypothesis H1: σ12 ≠ σ22. The re-sults of the calculations for the model of surface rough-ness are presented in Table 6.Because p is greater than 0,05 there is no reason to reject the hypothesis of homogeneity of variances for each of the cases.261METALURGIJA 52 (2013) 2, 259-262G. KROLCZYK et al.: PREDICTING THE SURFACE ROUGHNESS IN THE DRY MACHINING…We subsequently studied two populations having normal distributions N (m1, σ1) and N (m2, σ2), standard deviations are unknown, but equal, i.e. there is σ1 = σ2. Based on two sample sizes n1 = 4 and n2 = 4 we veriﬁ ed the hypothesis H0: m1 = m2 against the alternative hy-pothesis H1: m1 ≠ m2. We calculated the average values from both samples  and  and variances s12 and s22, then the value of t statistics according to the following formula:  (2),The calculation results are presented in Table 7.turning the DSS. The following conclusions have been drawn:1.  Second-order model predicting equations for sur-face roughness have been developed using re-sponse surface methodology for turning the DSS with coated tools.2.  The established equations clearly show that the feed rate was main inﬂ uencing factor on the sur-face roughness. Surface roughness increased with increasing the feed rate.3.  The predicted values and measured values are fairly close which indicates that the developed surface roughness prediction model can be effec-tively used to predict the surface roughness for the turning process. Using such models, a remark-able cost saving has been obtained.Nomenclatureap  – depth of cut in mmf  – feed rate in mm/revvc – cutting speed in m/minRa – average of surface roughness in µmDSS – Duplex Stainless SteelREFERENCES[1] W. Olszak: Obróbka skrawaniem, WNT, Warszawa 2008.[2]  M. Alauddin, M. A. El Baradie and M. S. J. Hashmi: Pre-diction of tool life in end milling by response surface me-thodology, Journal of Materials Processing and Technolo-gy, 71 (1997), 456-465.Table 7  The results of t-statistic model calculations for surface roughnesst 0,1237p 0,9055The signiﬁ cance level of p for the tested models is greater than 0,05 which means there is no reason to re-ject the hypothesis of equal averages. Therefore, it is shown on the signiﬁ cance level of 0,05, that the average value of research and the model are not signiﬁ cantly different. It can therefore be concluded that the desig-nated model reﬂ ects changes in surface roughness rep-resented by the empirical values.CONCLUSIONSThe purpose of this article was to develop a method-ology which can offer the possibility of predicting the surface roughness in the turning process of duplex stain-less steel. Predicting the required parameter of surface roughness Ra in the process of dry machining is an im-portant part of the process and impact of such condi-tions on the technological properties of the surface lay-er. Tables 8 - 10 shows results (values) of the surface roughness of Ra, depending on the technological cut-ting parameters in the turning process of duplex stain-less steel.Factorial design of an experiment can be success-fully employed using coated carbide cutting tools in Table 8  Values of surface roughness according to Ra for Ra = f(ap) for vC = 50 - 150 m/min and f = 0,2 - 0,4 mm/rev obtained for the depths of cutting ap = 1 mm, ap = 2 mm, ap = 3 mmNo vC f Ra(ap=1) Ra(ap=2) Ra(ap=3)1 50 0,2 2,029 1,466 1,1342 0,3 4,366 3,949 3,7633 0,4 6,383 6,112 6,0724 100 0,2 1,653 1,321 1,2215 0,3 4,228 4,043 4,0886 0,4 6,484 6,444 6,6357 150 0,2 0,996 0,896 1,0268 0,3 3,809 3,855 4,1329 0,4 6,304 6,495 6,918Table 9  Values of surface roughness according to Ra for Ra = f(vC) for f = 0,2 - 0,4 mm/rev and ap = 1 - 3 mm obtained for the cutting speed vC = 50 m/min, vC = 100 m/min, vC = 150 m/minNo f ap Ra(vc=50) Ra(vc=100) Ra(vc=150)1 0,2 1 2,029 1,653 0,9962 2 1,466 1,321 0,8963 3 1,134 1,221 1,0264 0,3 1 4,366 4,228 3,8095 2 3,949 4,043 3,8556 3 3,763 4,088 4,1327 0,4 1 6,383 6,484 6,3048 2 6,112 6,444 6,4959 3 6,072 6,635 6,918Table 10  Values of surface roughness according to Ra for Ra = f(f) for vC = 50 - 150 m/min and ap = 1 - 3 mm obtained for the feed f = 0,2 mm/rev, f = 0,3 mm/rev, f = 0,4 mm/revNo vC ap Ra(f=0,2) Ra(f=0,3) Ra(f=0,4)1 50 1 2,029 4,366 6,3832 2 1,466 3,949 6,1123 3 1,134 3,763 6,0724 100 1 1,653 4,228 6,4845 2 1,321 4,043 6,4446 3 1,221 4,088 6,6357 150 1 0,996 3,809 6,3048 2 0,896 3,855 6,4959 3 1,026 4,132 6,918262  METALURGIJA 52 (2013) 2, 259-262G. KROLCZYK et al.: PREDICTING THE SURFACE ROUGHNESS IN THE DRY MACHINING…[3]  M. Hasegawa, A. Seireg and R. A. Lindberg:. Surface rou-ghness model for turning, Tribology International, (De-cember) (1976), 285–289.[4]  G. T. Smith: Cutting Tool Technology, Industrial Handbo-ok, Springer - Verlag, London 2008.[5]  Y. Sahin, A. Riza Motorcu: Surface roughness model for machining mild steel with coated carbide tool, Materials & Design, 26 (2005), 321–326.[6] K. Taraman: Multi-machining output-multi independent variable turning research by response surface methodolo-gy, Int. J. Prod. Res., 12 (1974) 2, 233–245.[7]  R. M. Sundaram, B. K. Lambert: Mathematical models to predict surface ﬁ nish in ﬁ ne turning of steel, Int. J. Prod. Res., 19 (1981), 557–564.[8]  M. A. El Baradie: Surface roughness prediction in the tur-ning of high strength steel by factorial design of experi-ments, J. Mater. Process. Tech., 67 (1997), 55–61.[9] A. Mital, M. Mehta: Surface ﬁ nish prediction models for ﬁ ne turning, Int. J. Prod. Res., 26 (1998) 12, 1861–1876.[10] J. Kopac, M. Bahor, M. Sokovic: Optimal machining para-meters for achieving the desired surface roughness in ﬁ ne turning cold pre-formed steel work processing, Int. Mach. Tool. Manuf., 42 (2002), 707–716.[11]  Z. Polański: Metody optymalizacji w technologii maszyn, PWN, Warszawa 1977.[12] D. Montgomery: Design and Analysis of Experiments, 5th Edition, John Wiley & Sons Inc., New York, 2003.[13] StatSoft, Inc., STATISTICA (data analysis software sy-stem), version 9.0. www.statsoft.com, 2009.Note:  The responsible translator for English language is lecturer from Faculty of Production Engineering and Logistics, Opole Univer-sity of Technology, Poland

Predicting the surface roughness in the dry machining of duplex stainless steel (DSS)

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259METALURGIJA 52 (2013) 2, 259-262G. KROLCZYK, S. LEGUTKO, M. GAJEKPREDICTING THE SURFACE ROUGHNESS IN THE DRY MACHINING OF DUPLEX STAINLESS STEEL (DSS)Received – Prispjelo: 2012-06-17Accepted – Prihvaćeno: 2012-10-25Preliminary note – Prethodno priopćenjeISSN 0543-5846METABK 52(2) 259-262 (2013)UDC – UDK 669.15:621.773:621.9.015=111G. Krolczyk, M. Gajek, Opole University of Technology, Department of Technology and Work Environment Engineering, Opole, Poland, S. Le-gutko, Poznan University of Technology, Institute of Mechanical Tech-nology, Poznan, PolandThis paper examines the infl uence of cutting parameters, namely cutting speed, feed and depth of cut onto surface roughness after DSS turning process. The study included developing a mathematical model t o determine the sur-face roughness. Verifi cation research has been carried out on CNC lathe; henc e the test plan has been adjust ed to the possibility of programmable machines controlling GE Fanuc Series 0-T. The comparison of results obtained by given experimental plan was performed in industrial company.Key words: Duplex Stainless Steel, dry machining, surface roughness, response surface method (RSM)INTRODUCTIONDuplex stainless steel is gaining importance accord-ing to companies producing construction materials. It is refl ected in the wide range of these products available in the market. According Olszak [1] DSS is classifi ed as almost unworkable. The wear process of tool wedges, which is largely dependent on cutting parameters, is an important factor. Investigation performed by Alauddin et al. [2] has revealed that when the cutting speed is in-creased, productivity can be maximised and surface quality can be improved. The wear of the cutting tool wedge leads to a deterioration in quality of the machined surface, and in the most commonly used surface rough-ness parameter in production which is the arithmetic average deviation from the average line profi le. Surface fi nish can be characterised by various parameters and mostly such as surface roughness Ra [3]. The basic re-quirement in the application of indexable tool inserts in industrial conditions is the total increase in production; not the precision performance of its particular machine parts. According to Smith [4], where the equipment stocks are consolidated and the materials used in cutting tools are more universal, we can, in industrial condi-tions, use a smaller number of types and geometry of the cutting tool. Smaller stocks of indexable tool inserts allow us to more effectively optimize the production process. The above-mentioned aspects, combined with the optimization of the cutting speed, feed and depth of cut, allow the desired production targets to be met. Due to an optimization of the cutting parameters, it is possi-ble to take full advantage of the basic equipment; as a result you can expect a large increase in overall produc-tion effi ciency. In order to know surface quality and di-mensional properties is necessary to employ theoretical models making it feasible to do predictions in function Table 3 Coded indication of the study planTestNo.Coded factors Decoded real valuex1 x2 x3 vC f ap 1 -1 -1 -1 70 0,24 1,42 -1 -1 +1 70 0,24 2,63 -1 +1 -1 70 0,36 1,44 -1 +1 +1 70 0,36 2,65 +1 -1 -1 130 0,24 1,46 +1 -1 +1 130 0,24 2,67 +1 +1 -1 130 0,36 1,48 +1 +1 +1 130 0,36 2,69 -1,682 0 0 50 0,3 210 1,682 0 0 150 0,3 211 0 -1,682 0 100 0,2 212 0 1,682 0 100 0,4 213 0 0 -1,682 100 0,3 114 0 0 1,682 100 0,3 315 0 0 0 100 0,3 216 0 0 0 100 0,3 217 0 0 0 100 0,3 218 0 0 0 100 0,3 219 0 0 0 100 0,3 220 0 0 0 100 0,3 2Table 1  Chemical composition of 1.4462 duplex stainless steel /wt %Cmax Simax Mnmax Pmax Smax Cr Ni Mo N0,03 1 2 0,03 0,02 21 - 23 4,5 - 6,5 2,5 - 3,5 0,1 -0,22Table 2 Cutting tool specifi cationTool Substrate OthersT1 Hardness: 1 350 HV3Grade: M25, P35Coatings: Ti(C,N) - (2 µm) (top layer)Al2O3 - (1,5 µm) (middle layer)TiN - (2 µm) (bottom layer)Coating technique: CVD260  METALURGIJA 52 (2013) 2, 259-262G. KROLCZYK et al.: PREDICTING THE SURFACE ROUGHNESS IN THE DRY MACHINING…of operation conditions. The response surface method (RSM) is practical, economical and relatively easy for use [5]. The experimental data was utilized to build mathematical model for second-order model. This method has been used by some researchers for determi-nation of tool life and surface roughness [3, 6–10]. Ta-raman [6], Hasegawa et al. [3], Sundaram and Lambert [7] used the RSM for predicting surface roughness. Baradie [8], Mital and Mehta [9], Kopac et al. [10] in-vestigated the use of RSM in developing a surface roughness prediction model.EXPERIMENTAL TECHNIQUESWorkpiece and cutting tool materialsMachined material was 1.4462 (DIN EN 10088-1) steel with a ferritic-austenitic structure containing about 50 % of austenite. The ultimate tensile strength Rm = 700 MPa, Brinell hardness - 293 HB. The elemental composition of the machined material and technical de-tails of the cutting tools are given in Tables 1 and 2 re-spectively. Cutting tool inserts of TNMG 160408 desig-nation clamped in the tool shank of ISO-MTGNL 2020-16 type were employed. Based on the industry recom-mendations a range of cutting parameters T1: vc = 50 - 150 m/min, f = 0,2 - 4 mm/rev, ap = 1 - 3 mm was se-lected. The study was conducted within a production facility. The research program was carried out on a lathe CNC 400 Famot – Pleszew.Research planThe static determined selective-multivariate uniform static - rotatable PS/DS-P:λ program as the method of optimization for DSS cutting parameters has been se-lected [11]. A choice of the PS/DS-P:λ program was dictated with the assumption that the second-degree polynomial function model will be a nonlinear model which can be reduced to a linear model. The second-degree polynomial function has been chosen because there are no restrictions in research related to the meas-urement technique. The required number of experimen-tal points is N = 23 + 6 + 6 = 20 (Table 3). There are eight factorial experiments (3 factors on two levels, 23) with added 6 star points and centre point (average level) re-peated 6 times to calculate the pure error [12].MODELING OF SURFACE ROUGHNESS AND ITS VERIFICATIONThe aim of this study was an attempt to verify, in industrial conditions, the calculated value of the func-tion describing surface roughness after turning DSS. The study was conducted in industrial conditions. Cho-sen results are presented in Table 4.Surface roughness modelBased on the PS/DS-P:λ program and the experi-mental data the polynomial function of surface rough-ness has been elaborated:Ra =  – 1,7163 – 0,013259·vC + 27,5189·f – –1,4311·ap – 0,000056162·vC2 – –15,9793·f2 + 0,11521·ap2 + +0,047667 vC ·f + 0,0046265 vC ·ap + +1,4583 f·ap.    (1)Verifi cation study of modelThe evaluation of the mathematical model was per-formed using the Student’s t test to compare two mean values of populations with normal distributions and ho-mogeneous variances. Statistical calculations were per-formed with the Statistica 9.0 program [13].The assumptions of normality were examined using the Shapiro - Wilk test for the model of surface rough-ness (Table 5).Table 4  Verifi ed research parameters of the model of surface roughnessCutting parameters Average value of research Ra /µmCalculation resultsRa /µmvC / m/min f /mm/rev aP /mm150 0,2 2 1,92 0,90150 0,4 2 6,49 6,5050 0,2 2 1,65 1,4750 0,4 2 5,89 6,11Table 5  Tests of normality for the model of surface roughnessVariable n W pAverage value of research 4 0,8100 0,1215Calculation results 4 0,8070 0,1154Table 6  The results of F-statistic model calculation for surface roughnessF 1,3482p 0,8118As the level of signifi cance of p is greater than 0,05 for the test case, there is no reason to reject the hypoth-esis of normal distribution.Homogeneity of variances was checked by Fisher test. Two general populations has been examined with normal distributions N (m1, σ1) and N (m2, σ2), where the parameters of these distributions are unknown. There were two sample sizes n1 = 4 and n2 = 4. On the basis of test results the hypothesis was tested H0: σ12 = σ22, against the alternative hypothesis H1: σ12 ≠ σ22. The re-sults of the calculations for the model of surface rough-ness are presented in Table 6.Because p is greater than 0,05 there is no reason to reject the hypothesis of homogeneity of variances for each of the cases.261METALURGIJA 52 (2013) 2, 259-262G. KROLCZYK et al.: PREDICTING THE SURFACE ROUGHNESS IN THE DRY MACHINING…We subsequently studied two populations having normal distributions N (m1, σ1) and N (m2, σ2), standard deviations are unknown, but equal, i.e. there is σ1 = σ2. Based on two sample sizes n1 = 4 and n2 = 4 we verifi ed the hypothesis H0: m1 = m2 against the alternative hy-pothesis H1: m1 ≠ m2. We calculated the average values from both samples  and  and variances s12 and s22, then the value of t statistics according to the following formula:  (2),The calculation results are presented in Table 7.turning the DSS. The following conclusions have been drawn:1.  Second-order model predicting equations for sur-face roughness have been developed using re-sponse surface methodology for turning the DSS with coated tools.2.  The established equations clearly show that the feed rate was main infl uencing factor on the sur-face roughness. Surface roughness increased with increasing the feed rate.3.  The predicted values and measured values are fairly close which indicates that the developed surface roughness prediction model can be effec-tively used to predict the surface roughness for the turning process. Using such models, a remark-able cost saving has been obtained.Nomenclatureap  – depth of cut in mmf  – feed rate in mm/revvc – cutting speed in m/minRa – average of surface roughness in µmDSS – Duplex Stainless SteelREFERENCES[1] W. Olszak: Obróbka skrawaniem, WNT, Warszawa 2008.[2]  M. Alauddin, M. A. El Baradie and M. S. J. Hashmi: Pre-diction of tool life in end milling by response surface me-thodology, Journal of Materials Processing and Technolo-gy, 71 (1997), 456-465.Table 7  The results of t-statistic model calculations for surface roughnesst 0,1237p 0,9055The signifi cance level of p for the tested models is greater than 0,05 which means there is no reason to re-ject the hypothesis of equal averages. Therefore, it is shown on the signifi cance level of 0,05, that the average value of research and the model are not signifi cantly different. It can therefore be concluded that the desig-nated model refl ects changes in surface roughness rep-resented by the empirical values.CONCLUSIONSThe purpose of this article was to develop a method-ology which can offer the possibility of predicting the surface roughness in the turning process of duplex stain-less steel. Predicting the required parameter of surface roughness Ra in the process of dry machining is an im-portant part of the process and impact of such condi-tions on the technological properties of the surface lay-er. Tables 8 - 10 shows results (values) of the surface roughness of Ra, depending on the technological cut-ting parameters in the turning process of duplex stain-less steel.Factorial design of an experiment can be success-fully employed using coated carbide cutting tools in Table 8  Values of surface roughness according to Ra for Ra = f(ap) for vC = 50 - 150 m/min and f = 0,2 - 0,4 mm/rev obtained for the depths of cutting ap = 1 mm, ap = 2 mm, ap = 3 mmNo vC f Ra(ap=1) Ra(ap=2) Ra(ap=3)1 50 0,2 2,029 1,466 1,1342 0,3 4,366 3,949 3,7633 0,4 6,383 6,112 6,0724 100 0,2 1,653 1,321 1,2215 0,3 4,228 4,043 4,0886 0,4 6,484 6,444 6,6357 150 0,2 0,996 0,896 1,0268 0,3 3,809 3,855 4,1329 0,4 6,304 6,495 6,918Table 9  Values of surface roughness according to Ra for Ra = f(vC) for f = 0,2 - 0,4 mm/rev and ap = 1 - 3 mm obtained for the cutting speed vC = 50 m/min, vC = 100 m/min, vC = 150 m/minNo f ap Ra(vc=50) Ra(vc=100) Ra(vc=150)1 0,2 1 2,029 1,653 0,9962 2 1,466 1,321 0,8963 3 1,134 1,221 1,0264 0,3 1 4,366 4,228 3,8095 2 3,949 4,043 3,8556 3 3,763 4,088 4,1327 0,4 1 6,383 6,484 6,3048 2 6,112 6,444 6,4959 3 6,072 6,635 6,918Table 10  Values of surface roughness according to Ra for Ra = f(f) for vC = 50 - 150 m/min and ap = 1 - 3 mm obtained for the feed f = 0,2 mm/rev, f = 0,3 mm/rev, f = 0,4 mm/revNo vC ap Ra(f=0,2) Ra(f=0,3) Ra(f=0,4)1 50 1 2,029 4,366 6,3832 2 1,466 3,949 6,1123 3 1,134 3,763 6,0724 100 1 1,653 4,228 6,4845 2 1,321 4,043 6,4446 3 1,221 4,088 6,6357 150 1 0,996 3,809 6,3048 2 0,896 3,855 6,4959 3 1,026 4,132 6,918262  METALURGIJA 52 (2013) 2, 259-262G. 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Mehta: Surface fi nish prediction models for fi ne turning, Int. J. Prod. Res., 26 (1998) 12, 1861–1876.[10] J. Kopac, M. Bahor, M. Sokovic: Optimal machining para-meters for achieving the desired surface roughness in fi ne turning cold pre-formed steel work processing, Int. Mach. Tool. Manuf., 42 (2002), 707–716.[11]  Z. Polański: Metody optymalizacji w technologii maszyn, PWN, Warszawa 1977.[12] D. Montgomery: Design and Analysis of Experiments, 5th Edition, John Wiley & Sons Inc., New York, 2003.[13] StatSoft, Inc., STATISTICA (data analysis software sy-stem), version 9.0. www.statsoft.com, 2009.Note:  The responsible translator for English language is lecturer from Faculty of Production Engineering and Logistics, Opole Univer-sity of Technology, Poland

Predicting the surface roughness in the dry machining of duplex stainless steel (DSS)

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