Design optimization of cutting parameters for turning of AISI 304 austenitic stainless steel using Taguchi method
Atul Kulkarni*, Girish Joshi & V G Sargade
Department of Mechanical Engineering, Dr Babasaheb Ambedkar Technological University, Lonere 402 103, India Received 17 August 2012; accepted 1 April 2013
In the present work Taguchi method is used to optimize cutting parameters during dry turning of AISI 304 austenitic stainless steel with AlTiCrN coated tool. The coating was deposited on fine-grained K-grade (ISO K-20) cemented carbide cutting insert using physical vapor deposition (PVD) technique. The turning parameters evaluated are cutting speed of 200 and 260 m/min, feed rate of 0.20 and 0.26 mm/rev, coating thickness of 3.6 µm and 4.6 µm each at two levels.
The analysis of results shows that the optimal combination of process parameters is obtained at 260 m/min cutting speed, 0.20 mm/rev feed and 4.6 µm coating thickness for minimum cutting force. It is observed that cutting speed plays an important role in minimization of cutting force and coating thickness plays an important role in minimizing average flank wear (VB). A multiple linear regression models are developed for cutting force and average flank wear. The correlation coefficient is found to be more than 0.95, which shows that the developed model is reliable and could be used effectively for predicting the cutting force and average flank wear for the given tool and work material pair and within the domain of the cutting parameters.
Keywords: Taguchi, SS304 machining, Coating thickness, PVD, AlTiCrN coating
Austenitic stainless steel is one of the highly consumed steel worldwide and it is commonly used to fabricate chemical and food processing equipment, as well as machinery parts requiring high corrosion resistance. It is also amongst the “difficult-to-cut”
material and the difficulties such as poor surface finish and high tool wear are common. The work hardening and low thermal conductivity is recognized to be responsible for the poor machinability of AISI 304 austenitic stainless steels. In addition, they bond very strongly to the cutting tool during cutting and when chip is broken away, it may bring with it a fragment of the tool, particularly when cutting with cemented carbide tools1-4. Little work has been reported on the determination of optimum machining parameters during dry turning of austenitic stainless steels5. A very few researchers have developed a mathematical model for process parameters on hard turning of AISI 316 and AISI 202 stainless steel using CVD and PVD coated tools. Some have developed empirical models for tool life, surface roughness and cutting force prediction over a selected range of cutting parameters as well6,7. Surface roughness and tool wear were also predicted by regression analysis
and ANOVA theory8,9. In addition, some experiments were conducted to evaluate the performance of AISI304 steel on auto sharpening machine by using Taguchi method. Results revealed that tools shape and feed are significant factors during machining10.
Taguchi’s parameter design is an important tool for robust design. It offers a simple and systematic approach to optimize design for performance, quality and cost. Signal to noise ratio and orthogonal array are two major tools used in robust design. Signal to noise ratio, which measures quality with emphasis on variation and orthogonal arrays accommodates many design factors simultaneously11,12. Taguchi method offers quality to the product and is measured by quality characteristics such as: nominal is the best, smaller is better and larger is better 11,13. Optimization using Taguchi method in high speed turning uses conceptual S/N ratio approach and Pareto ANOVA method. The Taguchi’s robust design method is well suitable to analyze the metal cutting problem.
The S/N ratio characteristics can be divided into three categories when the characteristic is continuous:
(i) Larger the better
∑
−
= 1 12
log
10 n y
N S
——————
*Corresponding author (E-mail: apk_31173@rediffmail.com)
(ii) Nominal the best
2 2
log 10 S
Y N
S =
(iii) Smaller the better
{ }
2log1
10
∑
−
= Y
n N
S
Where y is the average observed data, Sy2 the variance of y, n the number of observations.
The S/N ratio for each level of process parameters is computed based on the S/N analysis. Regardless of the category of the quality characteristic, a greater S/N ratio corresponds to better quality characteristics.
Therefore, the optimal level of the process parameters is the level with the greatest S/N ratio. Furthermore, a statistical analysis of variance (ANOVA) is performed to see which process parameters are statistically significant. With the S/N and ANOVA analyses, the optimal combination of the process parameters can be predicted11-14.
Many researchers have carried out experimental investigations over the years in order to study the effect of cutting parameters, tool geometries, tool coating on the work-pieces machinability using several types of work-piece materials. Tool geometry and appropriate tool coating plays an important role in machining. In fact the function of tool coating is to minimize the tool wear, increase the hot hardness, oxidation resistance and thermal stability.
Chemical vapor deposition and physical vapor deposition are the two coating processes which provide different tool coating for the different materials. Many researchers have tried different CVD coatings in the study of machinability of AISI 304 stainless steel. However, CVD coatings are used in wet machining conditions. In wet machining, the scrap handling is also the major challenge15-18.
TiAlN PVD coating material was introduced with excellent hardness and thermal stability. This TiAlN PVD coating produced by CAE and UBMS technique is commonly used for AISI 304 austenitic stainless steel because it has high oxidation resistance (900oC) and high hot hardness 3300 HV19-24. Addition of Cr in (Ti, Al)N coating further increases the oxidation resistance (1100oC) by forming Cr2O3 film on the surface. Also, CrN sub layer reduces stress and improves adhesion which enhances mechanical
properties25-27. The coating thickness plays important role in tool performance. In the coated tool, the appropriate coating thickness improves the adhesion and reduces residual stress which increases the tool life28.
This paper describes the dry turning of AISI 304 austenitic stainless steel with parameters of turning at two levels and three factors each. The spindle speed is considered in the range of 1160 to 1380 rpm.
The main objective of this work is to understand the influence of cutting parameter on the cutting force and average flank wear (VB) during turning of AISI 304 work material using ALTiCrN coated tool.
Experimental Procedure
The machining trials were performed by AlTiCrN coated inserts for continuous turning of AISI 304 austenitic stainless steel with chemical composition shown in Table 1. The work-piece specimens were 300 mm long and 60 mm in diameter. The turning tests were carried for 50 mm of cutting length. The spindle speed varied from 1060 rpm to 1380 rpm.
ACE CNC LATHE JOBBER XL was used for conducting the machining trials.
The experiments were carried out with three factors at two levels each as shown in Table 2. The fractional factorial design used is a standard L8 orthogonal array with 20 degree-of-freedom1. This orthogonal array is chosen due to its capability to check the interactions among factors.
The fine grained uncoated cemented carbide (K20) (KENNAMETAL Make) turning tools were coated with AlTiCrN hard coating. The AlTiCrN hard coating was obtained for CEMECON, Germany and IonBond, India. The ISO designation of insert and tool holder were CNMA120408 and PCLNL 2525M12 respectively. The properties of the AlTiCrN coating and K20 insert are shown in Table 3.
The elemental compositions of the cutting tools were also examined under a JEOL JSM 6360 LV type
Table 1—Chemical composition of the work-piece materials Elements C Si Mn P S Cr Mo Ni Al V
% by wt 0.065 0.591 1.197 0.034 0.024 18.53 0.21 8.75 0.001 0.025 Table 2—Factors and levels used in the experiment
Factors Level I Level II
A Cutting speed (m/min) 200 260
B Feed (mm/rev) 0.2 0.26
C Coating thickness (µm) 3.6 4.6
scanning electron microscope (SEM). Calo tester (ball dia. 20 mm) and Nikon measuring microscope was used to measure the thickness of the coating.
Figure 1 shows the surface EDAX image of AlTiCrN hard coating. Also, coating thickness is shown in Fig. 2.
Cutting force was measured with a Kistler 9257A three component piezoelectric dynamometer and associated 5019 B130 charge amplifiers connected to PC employing Kistler Dynoware force measurement software. The turning tests were conducted at two different cutting speeds (200 and 260 m/min) and feed rate (0.20 and 0.26 mm/rev) while depth of cut were kept constant at 1 mm. The results given in Table 4 are the average of 3 machining trials.
Results and Discussion
The objective of the experiment is to optimize the high speed turning of AISI 304 austenitic stainless steel parameters to get lower values of cutting force and average flank wear. Table 4 shows standard L8 orthogonal array and experimental result for cutting force, and average flank wear.
Fig. 1—EDAX profile of AlTiCrN coating/
Fig. 2—SEM fractograph and calo test image of AlTiCrN coated cemented carbide insert (a) 4.6 µm and (b) 3.6 µm Table 3—Properties of AlTiCrN coating and K20 cutting insert
HPPMS Coating Insert (Substrate)
Composition AlTiCrN Grade K20
Thermal stability (oC)
1100 Thermal stability (°C) 500 Microhardness
HV (0.05)
3500 Hardness (HRA) 92.1 Surface roughness
(µm)
0.3 TRS (Gpa) 2.06
Thermal conductivity (w/mK)
85 Table 4—L8 orthogonal array and experimental result for average
flank wear and cutting force Expt.
No.
Cutting speed, m/min
Feed, mm/rev
Coating thickness,
µm
Cutting force (Fc),
N
Average flank wear (VB),
µm
1 200 0.2 3.6 293 134
2 200 0.2 4.6 285 53
3 260 0.26 3.6 356 196
4 260 0.26 4.6 359 118
5 200 0.26 3.6 390 167
6 200 0.26 4.6 375 77
7 260 0.2 3.6 283 216
8 260 0.2 4.6 262 93
Cutting force analysis
Table 4 shows the actual data for cutting force (Fc) and average flank wear (VB). Table 5 shows response table for signal-to-noise ratios smaller is better for cutting force. Table 6 shows analysis of variance for cutting force using adjusted SS for tests. From Tables 5 and 6, it is observed that feed is the most significant parameter which has more influence on the cutting force. Its contribution is 92.63%. From Fig. 3a the main effect plot, it is found that the cutting speed and coating thickness are less significant as the slope gradient is small. Analysis reference table for S/N ratio (Tables 5 and 6) and Fig. 3a response suggested that choosing a cutting speed of 260 m/min, feed of 0.2 mm/rev and coating thickness of 4.6 µ m gives the cutting force within the range of experiments based on smaller the better characteristics. The interaction plot for S/N ratio is shown in Fig. 3b and it indicates the minimum interaction between the factors for the selected range of experiments. The mean effect plot (fitted means) for the cutting force is shown in Fig. 4.
It is seen that as cutting speed and coating thickness increases, there is considerable decrease in the mean cutting force whereas increase in feed increases in mean value of cutting force.
Average flank wear (VB) analysis
Flank wear has a detrimental effect on surface finish, residual stress and micro structural changes, shape of tool, cutting conditions. The flank wear is caused by the abrasive and adhesive actions between the cutting tool and the machined surface. Table 7
shows response table for signal-to-noise ratios smaller is better for average flank wear (VB). Table 8 shows analysis of variance for average flank wear (VB) using adjusted SS for tests. This data is plotted in Fig. 5.
From Tables 7 and 8, it is observed that coating thickness is the most significant parameter which has more influence on the average flank wear. Also, the contribution of cutting speed is 20% whereas the contribution of coating thickness is 74%. From Fig. 5a the main effect plot, it was found that the feed is less significant as the slope gradient is small.
Analysis reference table for S/N ration (Tables 7 and 8) and Fig. 5a response suggested that choosing
Table 5—Response table for signal-to-noise ratios smaller is better
Level Cutting speed Feed Coating thickness
1 -50.43 -48.96 -50.31
2 -49.88 -51.36 -50.01
Delta 0.55 2.4 0.29
Rank 2 1 3
Table 6—Analysis of variance for cutting force (Fc) using adjusted SS for tests
Source DF Seq SS Adj SS Adj MS F P % Contribution
Cutting speed (m/mm) 1 861.1 861.1 861.1 7.17 0.228 5.01
Feed (mm/rev) 1 15931.13 15931.13 15931.13 132.62 0.055 92.63
Coating thickness (µm) 1 210.1 210.1 210.1 1.75 0.412 1.22
Cutting speed*Feed 1 36.1 36.1 36.1 0.30 0.681 0.21
Cutting speed*Coating Thickness 1 3.1 3.1 3.1 0.03 0.898 0.02
Feed*Coating thickness 1 36.1 36.1 36.1 0.30 0.681 0.21
Error 1 120.1 120.1 120.1 0.7
Total 7 17197.86
Fig. 3—(a) Main effect plot and (b) interaction plot for S/N ratio (cutting force)
cutting speed 200 m/min, feed 0.2 mm/rev and coating thickness 4.6 µm gives the average flank wear within the range of experiments based on smaller the better characteristics. Figure 5b shows interaction plot for S/N ratio. It indicates minimum interaction between the factors for the selected range of experiment. In addition, Fig. 6 indicates that as coating thickness increases there is decrease in the mean flank wear whereas increase in feed and cutting speed increases the mean flank wear.
Correlation and confirmation test
The correlations between the factors, i.e., cutting speed, feed, coating thickness and cutting force and average flank wear are established. The correlations shown in Table 9 are established on lower the better characteristics to find out value of parameter which gives better performance during high speed turning of AISI 304 austenitic stainless steel.
Fig. 4—Main effect plot (fitted means) for cutting force Table 7—Response table for signal to noise ratios smaller is better
(Avg. flank wear VB)
Level Cutting speed Feed Coating Thickness
1 -39.8 -40.77 -44.88
2 -43.34 -42.37 -38.26
Delta 3.53 1.6 6.63
Rank 2 3 1
Table 8—Analysis of variance for average flank wear (VB) using adjusted SS for tests
Source DF Seq SS Adj SS Adj MS F P % Contribution
Cutting speed (m/mm) 1 4608.00 4608.00 4608.00 12.64 0.175 19.723072
Feed (mm/rev) 1 480.00 480.00 480.00 1.32 0.456 2.0566268
Coating thickness (µm) 1 17298.00 17298.00 17298.00 47.46 0.092 74.038564
Cutting speed*Feed 1 338.00 338.00 338.00 0.93 0.512 1.4467011
Cutting speed*Coating thickness 1 112.50 112.50 112.50 0.31 0.677 0.4815203
Feed*Coating thickness 1 162.00 162.00 162.00 0.44 0.626 0.6933893
Error 1 364.50 364.50 364.50 1.5601258
Total 7 23363.00
Fig. 5—(a) Main effect plot and (b) interaction plot for S/N ratio (avg. flank wear (VB))
Fig. 6—Main effect plot (fitted means) for average flank wear (VB)
Table 10 shows the cutting parameters and confirmation test results. It is observed that the calculated error value for cutting force varies from -1.98% to 4% whereas for the average flank wear lower value is 1.91% and higher value is 9.8%. Figure 7 shows the SEM wear photograph of the conformity test at different conditions.
Conclusions
The austenitic stainless steels come under the category of difficult to machine materials because of their low thermal conductivity and high mechanical and micro structural sensitivity to strain and stress rate. The following conclusions can be drawn from this study:
(i) The analysis of results show that the optimal combination of process parameters are obtained at 260 m/min cutting speed, 0.2 mm/rev feed and 4.6 µm coating thickness for minimum cutting force. For the average flank wear (VB), optimum parameters are cutting speed 200 m/min, feed 0.2 mm/rev and coating thickness 4.6 µm within the range of experiments based on smaller the better characteristics.
(ii) The study found that feed has a significant effect on the cutting force main effects and its contribution is 92% whereas the interactions between other parameters are very less. The coating thickness has less effect on the cutting force.
(iii) The coating thickness followed by cutting speed has significant effect on average flank wear.
It is clear that by increasing coating thickness, average flank wear and cutting force can be controlled. Its contribution is 74% whereas the contribution of cutting speed is 20%.
(iv) The confirmation tests show that the error associated to cutting force is less than the error associated with average flank wear.
(v) The validation tests confirm the above factors.
Acknowledgement
The authors gratefully acknowledge the financial support that was received from the Department of Science and Technology, Government of India under DST-FAST TRACK program for young scientist (SR/FTP/ETA-68/2009). The authors also thank CEMECON Germany, IonBond, India and VIIT, Pune for the support.
Table 9 – Equation for cutting force, and average flank wear
Sr.No Response Equation
1 Cutting force Fc = 105 – 0.346 Vc + 1488 f - 10.3 CT R2 = 98.9%
2 Average flank wear (VB) VB = 270 + 0.800 Vc + 258 f - 93.0 CT R2 = 95.8%
Where Fc - Cutting force, Vc - Cutting speed (m/min), f - Feed (mm/rev), CT - Coating thickness (µm).
Table 10—Machining parameters used for conformity test and result
Parameters Cutting force (N) Average flank wear (VB)
Expt.
No.
Cutting speed (m/mm)
Feed (mm/rev)
Coating thickness (µm)
Expt.
value
Regression model value
Error
%
Expt.
value
Regression model value
Error
%
1 220 0.24 3.6 342.13 348.92 -1.98 168.23 173.12 -2.91
2 240 0.26 4.6 370.4 361.46 2.41 112.3 101.28 9.81
3 260 0.2 4.6 275.56 265.26 3.74 99.89 101.80 -1.91
Fig. 7—SEM photograph of flank wear during the conformity test at (a) 220 m/min, 0.24 mm/rev, (b) 240 m/min, 0.26 mm/rev, and (c) 260 m/min 0.2 mm/rev
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