Experimental Studies in Machining Duplex Stainless Steel using Response Surface Methodology

Transcription

1 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 48 Experimental Studies in Machining Duplex Stainless Steel using Response Surface Methodology 1 M. Thiyagu, 2 L. Karunamoorthy, 3 N. Arunkumar 1 Research scholar (Ph.D) Department of Mechanical Engineering, Anna University, Chennai, India. 2 Professor and Head, Department of Mechanical Engineering, Anna University, Chennai, India 3 Professor and Head (Student Affairs), Department of Mechanical Engineering, St. Joseph s college of Engineering, Chennai, India Abstract-- Duplex stainless steel (DSS) comes under hard to machine material owing to its inherent properties. In this experimental study an attempt has made in turning DSS UNS (2205) with the objective of minimizing surface roughness and cutting force. The design of experiments and optimization were done using Box Behnken design (BBD) and Response surface methodology (RSM). The factors and levels considered for experimentation includes cutting speed, feed rate, depth of cut, and tool nose radius in three levels. A second-order response surface models developed for surface roughness and cutting force were used in predicting the response in the design space. The Analysis of variance (ANOVA) and R-Squared value reveals the developed models were significant. The experimental results obtained indicate that feed rate and cutting speed were the more influential factors for surface roughness. Surface roughness increases with increase in the feed rate and cutting speed. For cutting force feed rate and nose radius are the significant factors. In addition the models adequacy was validated using confirmation experiments. The prediction error accounts from to 0.55% for surface roughness and to 2.52% for cutting force. and relies heavily on the operators experience and the data provided by the machine tool builder and cutting tool supplier for the target material. Hence, the optimization of cutting conditions is of great importance where the economy and quality of a machined part play a key role. Common methods of evaluating machining performance in a turning operation are based on performance characteristics such as s u r f a c e r o u g h n e s s, t o o l life and cutting force. In conventional turning operations, these performance characteristics are strongly correlated with cutting parameters such as feed rate, cutting speed and depth of cut [2]. Index Term CUTTING FORCE, DUPLEX STAINLESS STEEL, RESPONSE SURFACE METHODOLOGY, SURFACE ROUGHNESS I. INTRODUCTION Duplex stainless steel (DSS) contains a mixed microstructure of about equal proportions of austenite and ferrite allowing a combination excellent mechanical properties and high corrosion resistance [1]. Currently, these steels are being used in replacement of austenitic stainless steels in several industrial applications like piping, desalination plants, vessels, valves & fittings, fasteners, heat exchangers and construction materials. High work hardening rate, low heat conductivity and high toughness have a synergistic effect in inducing machining difficulties in DSS. The new developments in cutting tools and improvements in coating technologies provides a combination of abrasion wear resistance and chemical stability at high temperature to meet the demands of application. The knowledge of cutting forces, tool wear, surface quality and cutting tool tip temperature developed in various machining processes under given cutting conditions is being a dominating criterion of material machinability, to both the designer, machine tools manufacturer, as well as to end user. In actual industrial environment, the selection of appropriate machining parameters and cutting tool parameters is difficult Fig. 1. Fundamental Turning operation G. Krolczyk et al. predicted surface roughness in machining duplex stainless steel grade with coated tools. Feed rate was main influencing factor on the surface roughness [3]. G. Krolczyk et al. studied cutting wedge wear examination during turning of duplex stainless steel. Increase of the cutting speed increases the intensity of wear of the cutting edge and flank wear is the predominant wear [4]. Basim A. Khidhir and Bashir Mohamed studied optimization of cutting force in turning of nickel based Hastelloy. The effect of feed rate on cutting force is much more evident than the effect of speed [5]. V.Suresh babu et al. developed empirical second order model for the predicting the surface roughness in machining EN24 alloy steel using response surface method. Cutting speed is main influencing factor on the surface roughness [6]. S.R.Chauhan and Kali Dass studied optimization of machining parameter cutting speed, feed rate, depth of cut, and approach angle with surface roughness and tangential cutting force as response variable using Response

2 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 49 surface methodology (RSM). The results obtained indicate that the surface roughness increases with increase in the cutting speed and the feed rate, whereas tangential force increases with increase in approach angle and depth of cut [7]. Ihsan Korkut et al. investigated the influence of cutting speed on tool wear and surface roughness in machining AISI 304 austenitic stainless steel. A decrease in tool wear was observed with increasing the cutting speed up to 180 m/min. Surface roughness (Ra) was also decreased with increasing the cutting speed [8]. N.Tosun, L.Ozler studied the effect of machining parameter on surface roughness and tool life in hot turning of high manganese steel. The results showed that cutting speed and feed rate were the dominant variables on multiple cutting performance characteristics [2]. B. C. Routara, A. Bandyopadhyay, P. Sahoo presented a desirability function approach to study the optimal combination of machining parameters for multiple performance characteristics of the surface roughness parameters in turning mild steel. The signal-to-noise ratio is employed to investigate the optimal combination of cutting parameters to yield maximum overall desirability [9]. Ahmet Hasçalık and Ulaş Çaydaş used Taguchi method in optimization of machining parameters on surface roughness and tool life in a turning of commercial Ti-6Al-4V alloy using CNMG insert cutting tools. The conclusions revealed that the feed rate and cutting speed were the most influential factors on the surface roughness and tool life [10]. Yigit Kazancoglu et.al investigated the multi-response optimization of the turning process for an optimal parametric combination to yield the minimum cutting forces and surface roughness with the maximum material-removal rate (MRR) using a combination of a Grey relational analysis (GRA) and the Taguchi method. The analysis-of-variance method (ANOVA) used to identify the significance factors [11]. Sahoo and Sahoo developed the mathematical model and parametric optimization for surface roughness in turning D2 steel using TiN coated carbide insert using Taguchi parameter design and RSM [12]. Work to date has shown that very little work has been carried on the determination of optimum machining parameters when machining DSS. This instigated to conduct machining study on DSS. In this study, turning tests were carried on UNS (2205) DSS under dry condition to determine the optimum machining parameters and cutting tool geometry. Turning is one of the fundamental machining processes, especially for the finishing of machined parts. RESPONSE SURFACE METHODOLOGY The RSM is practical, economical and relatively easy for use and it was used by lot of researchers for modeling, analysis and optimization of machining processes. RSM is a collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which a dependent variable y called response is influenced by several independent variables x 1, x 2, x k called factors and the objective is to optimize the response [13]. If all of these variables are assumed to be measurable, the response surface can be expressed as ( ) Optimizing the response variable y, it is assumed that the independent variables are continuous and controllable by the experimenter with negligible error. The response or the dependent variable is assumed to be a random variable. The efficiency of the response surface analysis is significantly influenced by selecting the proper choice of experimental designs. Two types of RSM are available for experimentation and they are Central Composite Design (CCD) and Box- Behnken Design (BBD) [13], [14].CCD can be used when a comparatively accurate prediction of all response variable averages related to quantities measured during experimentation. Box-Behnken Design is normally used when performing non-sequential experiments. That is, performing the experiment only once. Both of these methodologies can be used to develop second-order quadratic relationship between the experimental factors and the responses. These designs allow efficient estimation of the first and second order coefficients. Box-Behnken design involves fewer design points, they are less expensive to run than central composite designs with the same number of factors. Box-Behnken Design do not have axial points, thus we can be sure that all design points fall within the safe operating zone. In RSM analysis, the approximation of y was proposed using the fitted second-order polynomial regression model which is called the quadratic model. The quadratic model of y can be written as follows: Where y is the dependent variable (response), x i is independent variable (factors), a 0 is constant regression co-efficient, a i is first-order co-efficient, a ii is pure quadratic co-efficient, a ij is interaction term co-efficient, and ϵ is random error which is assumed to be normally independently distributed. In our study BBD has been utilized for the designing the plan of experiments. In our study BBD has been utilized for the designing the plan of experiments. II. EXPERIMENTAL DETAILS A. EXPERIMENTAL SETUP The turning experiments were carried out to obtain experimental data under dry machining conditions. In the present investigation, experiments have been carried out using all geared high precision lathe, NAGMATI-175 manufactured by Bharath Machine Tools, India. The spindle speed range from rpm and longitudinal feed range

3 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 50 from mm/rev. The arrangement of experimental setup is shown in Fig. 2. Kistler lathe tool dynamometer (Type 9257B+9403) in conjunction with Dynoware software and a personal computer was used to measure the three components of cutting force (i.e.) feed force (F x ), radial force (F y ) and main cutting force (Fz). The surface roughness average (Ra) was the average deviation of the profile from the centerline along the sampling length (L): Test Method: OES-ASTM E (2005) ( ) ( ) Where x is the profile direction and Y is the ordinate of the profile curve. The surface roughness of duplex stainless steel (UNS 31803) has been measured using Surtronic 3+ stylus type roughness tester manufactured by Taylor Hobson with a cut-off length of 0.8mm and resolution 0.01µm. The measurement set-up shown in Fig 3. B. WORK MATERIAL The material selected for study was UNS (2205) DSS of diameter 75mm and length 300mm. Material hardness was 229 BHN. The chemical composition and mechanical properties of work material has been shown in TABLE I & II. UNS (2205) TABLE I CHEMICAL COMPOSITION C% Mn% Si% S% P% Cr% Ni% Mo% Ti% V% Co% Nb% Pb% TABLE II MECHANICAL PROPERTIES Test Method : ASTM A a, IS UNS (2205) Tensile Strength, N/Sq.mm Yield Strength, N/Sq.mm Elongation on 40 mm G.L (%) Reduction Area,(%) Fig. 2 Experimental set-up C. CUTTING TOOL & HOLDER Cutting tool for experimentation was carbide insert with ISO specification CNMG SM grade 1115 Sandvik Coromant make with PVD multi-layer coating (TiAlN + Chromium Oxide). Turning Tool-Holder PCLNR M12 with 25x25mm Shank size, right hand use. D. EXPERIMENTAL DESIGN The proposed layout of the methodology is depicted in the Fig 4. The first step in model generation using RSM is the design of experiments. There is large number of parameters that could be considered for machining in turning process. It is evident from the review of literature that the following three machining parameters are the most widespread among the researchers and machinists to control the turning process [15], [16]. In the present study these are selected as design factors in addition to it tool nose radius (tool geometry) is selected as fourth parameter while other parameters have been assumed to be constant over the experimental domain. Fig. 3 Surface Roughness measurement set-up

4 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 51 Fig. 4. Proposed Research methodology As seen in Table III, the four cutting parameters cutting speed (Vc-m/min), feed (f- mm/rev), depth of cut (d-mm) and tool nose radius (Nr-mm) were raised to three levels as per BBD. The response surface design and analysis was performed using a design of experiment package, DESIGN EXPERT Version 7.0. The experimental design layout show in TABLE IV. The experiments were carried out randomly to minimize the effect of unexplained variability in the observed responses due to extraneous factors. Parameters TABLE III EXPERIMENTAL FACTORS AND LEVELS Units Factors Notations Low Level (-1) Level- Mean (0) High Level (-1) Cutting speed (Vc) m/min A Feed rate (f) mm/rev B Depth of cut (d) mm C Nose radius (Nr) mm D

5 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 52 TABLE IV DESIGN LAYOUT MATRIX AND EXPERIMENTAL RESULTS Machining Parameters Tool geometry Responses Run order Cutting speed (A) Feed rate (B) Depth of cut (C) Nose radius (D) Surface Roughness (Ra - µm ) Cutting Force (Fz - N) III. ANALYSIS The experimental data were used to develop the quadratic response surface model for surface roughness and cutting force. From TABLE V Model F-value of implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than indicate model terms are significant. In this case A, B, B2, D2 are significant model terms. Values greater than indicate the model terms are not significant. The determination co-efficient R- Squared is a measure of the degree of fit. When R-Squared approaches unity, the better-the-response model fits the actual data. From TABLE VI R-Squared value for the fit is The "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of From TABLE VII the "Lack of Fit F-value" of 0.26 implies the Lack of Fit is not significant relative to the pure error. There is a 96.32% chance that a "Lack of Fit F-value" this large could occur due to noise. Not significant Lack of Fit shows the model better fits the experimental data "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Our model ratio of indicates an adequate signal. This model can be used to navigate the design space.

6 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 53 Source TABLE V ANOVA FOR SURFACE ROUGHNESS MODEL Sum of squares Degree of freedom Mean Squares F Value p-value Prob > F Model < significant A-Cutting speed significant B-Feed < significant C-Depth of cut D-Nose Radius AB AC AD BC BD CD A B significant C D significant Residual Lack of Fit not significant Pure Error Cor Total TABLE VI SURFACE ROUGHNESS MODEL SUMMARY Source Stanadard deviation R- Squared Adjusted R-Squared Predicted R-Squared PRESS Adeq Precision Quadratic TABLE VII LACK OF FIT TESTS FOR SURFACE ROUGHNESS MODEL Source Sum of squares Degree of freedom Mean Squares F p-value Value Prob > F Linear model FI model Quadratic model Suggested Cubic model Pure Error For cutting force from TABLE VIII Model F-value of implies the model is significant. There is only a 0.04% chance that a "Model F-Value" this large could occur due to noise. For cutting force model B, D, BC, CD, C2, D2 are significant model terms. From TABLE IX the R-Squared value for the fit is "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of From TABLE X the "Lack of Fit F-value" of 0.25 implies the Lack of Fit is not significant relative to the pure error. There is a 96.55% chance that a "Lack of Fit F-value" this large could occur due to noise. "Adeq Precision" ratio of indicates an adequate signal. The quadratic model for surface roughness and cutting force in terms of coded factors is given below. ( ) ( ) ( )

7 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 54 Source TABLE VIII ANOVA FOR CUTTING FORCE MODEL Sum of squares Degree of freedom Mean Squares F Value p-value Prob > F Model Significant A-Cutting speed B-Feed Significant C-Depth of cut D-Nose Radius Significant AB AC AD BC Significant BD CD Significant A B C < Significant D Significant Residual Not Lack of Fit Significant Pure Error Cor Total TABLE IX CUTTING FORCE MODEL SUMMARY Source Standard deviation R-Squared Adjusted R-Squared Predicted R-Squared PRESS Adeq. Precision Quadratic Source TABLE X LACK OF FIT TESTS FOR CUTTING FORCE MODEL Sum of squares Degree of freedom Mean Squares F Value p- Value Prob > F Linear model FI model Quadratic model Suggested Cubic model Pure Error IV. DIAGONISTIC STUDIES In Fig.5 (a) and 5(b) the normal probability plots of the residuals for surface roughness and cutting force follows a straight line implying that the errors (residuals) were normally independently distributed. Fig. 6(a) and 6(b) depicts the plot of actual response value to the predicted for surface roughness and cutting force. All points fall evenly on both sides of the 45º line. From the analysis of contour plot 7(a) and 3D-plots 8(a), 8(b) for surface roughness the least value of surface roughness µm was evident at cutting speed 22 m/min, feed 0.051mm/rev, depth of cut 0.4mm and nose radius 0.8mm. From the analysis of contour plot 7(b) and 3D-plots 9(a), 9(b) for cutting force the least value of cutting force N was evident at cutting speed 49 m/min, feed 0.13mm/rev, depth of cut 0.8mm and nose radius 0.8mm.

8 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 55 Fig. 5(a) Normal probability plot of Residuals for Surface roughness Fig. 5(b) Normal probability plot of Residuals for cutting force

9 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 56 Fig. 6(a) Actual Vs Predicted value of Surface roughness Fig. 6(a) Actual Vs Predicted value of Cutting force

10 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 57 Fig. 7(a) Contour plot of two factor interaction A and B for Surface roughness Fig. 7(b) Contour plot of two factor interaction B and D for Cutting force

11 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 58 Fig. 8(a) 3-D plot Interaction of A and B for Surface roughness Fig. 8(b) 3-D plot Interaction of B and D for Surface roughness

12 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 59 Fig. 9(a) 3D- plot Interaction of B and C for Cutting force Fig. 9(b) 3D- plot Interaction of C and D for Cutting force

13 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 60 Three confirmation experiments were conducted to validate the adequacy of the developed quadratic models. In the prediction interval of 95% the predicted value and actual experimental value were compared and the residual and error percentage were calculated. The confirmation test results shown in TABLE XI and XII. The percentage error range lies between to 0.55% for surface roughness and to 2.52% for cutting force. The test results lies within the 95% prediction interval. TABLE XI CONFIRMATION TESTS Cutting speed (A) Machining parameter & Tool geometry Feed rate (B) Depth of cut (C) Nose radius (D) Surface roughness (Ra - µm) Actual Predicted Residual Error % Cutting speed (A) TABLE XII CONFIRMATION TESTS Machining parameter & Tool geometry Cutting force (Fz - N) Feed rate Depth of cut Nose radius (B) (C) (D) Actual Predicted Residual Error % IV. RESULTS AND CONCLUSION In this work, the second order response surface models for surface roughness and c u t t i n g force were developed to study the effect of machining parameters and tool geometry in turning of DSS UNS grade alloy. The machining trials were executed as per BBD layout matrix developed using RSM. The analysis and optimization were done using R S M. T he following conclusions were drawn based on this study. 1) The normal probability plot of residuals confirms that the errors are normally independently distributed in the design space. 2) The feed rate is the significant contributor to the better surface finish followed by the spindle speed and nose radius. Increase in feed rate increases surface roughness. This result is in line with the study made by G.Krolczyk et al. in machining duplex stainless steel. 3) The feed rate and nose radius are significant factors in minimizing cutting force followed by cutting speed. 4) The diagnostic analysis for surface roughness reveals the least value of surface roughness µm at cutting speed 22 m/min, feed 0.051mm/rev, depth of cut 0.4mm and nose radius 0.8mm. For cutting force the minimum value N was evident at cutting speed 49 m/min, feed 0.13mm/rev, depth of cut of 0.8mm and nose radius 0.8mm. 5) The developed model is adequate for predicting the response within the 95% prediction interval. The same was confirmed through three sets of confirmation experiments REFERENCES [1] Villalobos et al. (2009). Microstructural changes in SAF 2507 super duplex stainless steel produced by thermal cycle. Revista Matéria. 14( 3), pp [2] N.Tosun & L.Ozler. (2004). Optimization for hot turning operations with multiple performance characteristics. Int J Adv Manuf Technol. 23, pp [3] G. Krolczyk, S. Legutko, M. Gajek. (2013). Predicting the surface roughness in the dry machining of duplex stainless steel DSS). Metabk. 52(2), pp [4] Grzegorz Królczyk, Stanisław Legutko, Pero Raos. (2013). Cutting wedge wear examination during turning of duplex stainless steel. Tehnički vjesnik. 20(3), pp [5] Basim A. Khidhir, Bashir Mohamed. (2013). Selecting of Cutting Parameters from Prediction Model of Cutting Force for Turning Nickel Based Hastelloy C-276 Using Response Surface Methodology. European Journal of Scientific Research. 33(3), pp [6] V.Suresh babu et al. (2011). Investigation and validation of optimal cutting parameters for least surface roughness in EN24 with response surface method. International journal of engineering, science and technology. 3(6), pp [7] S.R.Chauhan, Kali Dass (2011). Optimization of Machining Parameters in Turning of Titanium (Grade-5) Alloy Using Response Surface Methodology. Materials and manufacturing processes 27(5), pp [8] Ishan Korkut et al. (2004). Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel. Materials and Design, 25, pp [9] B. C. Routara, A. Bandyopadhyay, P. Sahoo, Use of desirability function approach for optimization of multiple performance characteristics of the surface roughness parameters in CNC turning, in ICME2007, Dhaka, Bangaladesh, 2007, pp. 1-7 [10] Ahmet Hascalik, Ulas caydas. (2007). Optimization of turning parameters for surface roughness and tool life based on the Taguchi method. Int J Adv Manuf Technol, 38, pp [11] Yigit Kazancoglu et al. (2007). Multi-objective optimization of the cutting forces in turning operations using the grey-based Taguchi method. MTAEC9, 45(2), pp

14 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 61 [12] A.K Sahoo, B. Sahoo. (2011). Surface roughness model and parametric optimization in finish turning using coated carbide insert: Response surface methodology and Taguchi approach. International Journal of Industrial Engineering Computations, 2, [13] Douglas C. Montgomery, Response surface methods and other approaches to Process optimization in Design and analysis of Experiments, 5 th ed. India: Wiley-India, 2010, pp [14] John A. Cornell, Design for Fitting Second-Degree Models in How to apply response surface methodology, 2 nd ed., vol. 8, USA: ASQC, 1990, pp [15] S.S.K. Deepak (2012). Application of different optimization methods for metal cutting operation A review. Research Journal of Engineering Sciences 1(3), pp [16] L.B. Abhang, M.Hameedullah (2011). Determination of optimum parameters for multi-performance characteristics in turning by using grey relational analysis. Int J Adv Manuf Technol, 63(1-4), pp AUTHOR S INFORMATION M. Thiyagu, Research scholar (Ph.D), Department of Mechanical Engineering, Anna University, Chennai, India. He received his Bachelor of Engineering (Production Engineering) from Bharathidasan University, Trichy, India in 2002 and Master of Technology (Industrial Engineering) from National Institute of Technology, Trichy, India in He is pursuing his Doctor of Philosophy in the Department of Mechanical Engineering, Anna University, Chennai, India. His research area includes Metal cutting, Process optimization. He has published two papers in International conferences and two papers in International journals. Currently working as Assistant Professor in Department of Mechanical Engineering, Agni college of Technology, Chennai, India. L. Karunamoorthy, Professor and Head, Department of Mechanical Engineering, Anna University, Chennai, India. He received his Doctor of Philosophy from MIT, Anna University, Chennai, India in His research area includes Metal cutting, Automation, Composite materials, and Nano materials. He has published more than 60 papers in International and National journals and more than 30 papers in International and National conferences. He is a active review members in many international journals. N. Arunkumar, Professor and Head (Student Affairs), Department of Mechanical Engineering, St. Joseph s college of Engineering, Chennai, India. He received his Doctor of Philosophy from, Anna University, Chennai, India in His research area includes Optimization techniques, Metal cutting, and Supply chain management. He has published 16 papers in International and National journals and 12 papers in International and National conferences.