Int J Adv Manuf Technol (2011) 56:455–462 DOI 10.1007/s00170-011-3198-5
ORIGINAL ARTICLE
Multi-response optimization of machining parameters in hot turning using grey analysis S. Ranganathan & T. Senthilvelan
Received: 30 August 2010 / Accepted: 24 January 2011 / Published online: 8 February 2011 # Springer-Verlag London Limited 2011
Abstract This paper envisages the multi-response optimization of machining parameters in hot turning of stainless steel (type 316) based on Taguchi technique. The workpiece heated with liquid petroleum gas flame burned with oxygen was machined under different parameters, i.e., cutting speed, feed rate, depth of cut, and workpiece temperature on a conventional lathe. The effect of cutting speed, feed rate, depth of cut, and workpiece temperature on surface roughness, tool life, and metal removal rate have been optimized by conducting multi-response analysis. From the grey analysis, a grey relational grade is obtained and based on this value an optimum level of cutting parameters has been identified. Furthermore, using analysis of variance method, significant contributions of process parameters have been determined. Experimental results reveal that feed rate and cutting speed are the dominant variables on multiple performance analysis and can be further improved by the hot turning process. Keyword Hot turning . Surface roughness . Tool life . Metal removal rate . Multiple optimization . S/N ratio . Grey analysis . ANOVA S. Ranganathan (*) : T. Senthilvelan Department of Mechanical Engineering, Sri Chandrasekharendra Saraswathi Viswa MahaVidyalaya, Enathur, Kanchipuram 631561, India e-mail:
[email protected] T. Senthilvelan e-mail:
[email protected] S. Ranganathan : T. Senthilvelan Department of Mechanical Engineering, Pondicherry Engineering College, Pondicherry 605104, India
1 Introduction It is difficult to obtain good surface finish and longer tool life while working with materials having high strength, corrosive resistance, toughness, and wear resistance in conventional machining. In order to circumvent the above problem, optimized cutting parameters and proper selection of cutting tools are to be employed. Stainless steel (type 316) is one such material with excellent wear resistance, corrosive resistance, and good strength and, hence, has wide applications in chemical, mining, pharmaceutical, petroleum, and nuclear industries. The chemical composition of stainless steel (type 316) is given in Table 1. The high operating temperature in hot turning process imparts softness on the material under investigation, which eases the machining process and further reduces the high cost of changing and sharpening cutting tools. Softening of the workpiece in hot machining is a more effective method than strengthening the cutting tool in conventional machining. Earlier research has shown that the selection of a proper heating method eliminates the undesirable structural changes in the workpiece and reduces the machining cost [1]. Chen and Lo [2] presented the results of an experimental investigation into the factors affecting tool wear in a direct current method of hot machining alloy steels, and considerable improvement in tool life was recorded. Wang and Lan [3] studied the parametric optimization of multi-objective precision turning using grey relational analysis and proposed optimum process parameters of precision CNC turning. The tool life can be improved by an external magnetic field in hot turning of materials which possess challenges during machining [4]. In hot machining of manganese steel using liquid petroleum gas (LPG), the tool life is increased by the selection of proper cutting speed and feed rate, whereas depth of cut and workpiece temperature
456 Table 1 Chemical composition of stainless steel (type 316)
Int J Adv Manuf Technol (2011) 56:455–462 C—0.057% P—0.034%
Mn—1.31% Ni—10.07%
play minor roles on tool life [5]. Tosum and Ozler [6] computed the tool life during hot machining of manganese steel using artificial neural network and regression analysis method and reported that the cutting speed, feed rate, and workpiece temperature around 600°C yield the longest tool life for carbide insert. Grey relational analysis based on grey system theory is the solution for solving the problem of complicated interrelationships among the multi-response. In grey-based Taguchi method, a multi-response process optimization problem can be converted to a single-response optimization problem where overall grey relational grade serves as the single objective function or response function to be optimized [7]. The proposed methodology of combining grey relational analysis and Taguchi method has wide applications in multi-response problems like optimization of machining parameters in hot turning of stainless steel (type 316) using the tungsten carbide (WC) insert. The WC insert has been chosen for this work because of abundant availability and economic reasons. In the present work, stainless steel (type 316) was heated with the flame produced by the mixture of LPG and oxygen and was machined under different cutting parameters, viz., cutting speed (Vc), feed rate (fs), depth of cut (ap), and workpiece temperature (°C) on an all geared lathe. Experimental details using the Taguchi method of parametric design have been employed for optimizing multiple performance characteristics such as surface roughness, tool life, and metal removal rate. Therefore, the grey relational analysis has been considered for the optimization of multiple response characteristics. Finally, analysis of variance (ANOVA) and confirmation test have been conducted to validate the predicted values.
2 Experimental procedure The hot turning experimental work was carried out on an all geared lathe machine with a maximum speed of 1,200 rpm and a 6-kW drive motor and manufactured by Padmini Engineering (p) India, Ltd. Figure 1 gives the details of all geared lathe machine with a workpiece and an LPG heating system. The workpiece was subjected to heating to three different temperatures, i.e., 200°C, 400°C, and 600°C, respectively. The hot turning tests were performed on the workpiece at different cutting speeds, feed rates, and depths of cut. The summary of experimental conditions has been listed in Table 2. A WC insert was used to turn the
Si—0.4% Cr—16.08%
S—0.025% Mo—2.22%
Cu—0.22% Remaining Fe
workpiece and stainless steel (type 316) with 30-mm diameter was chosen as the workpiece material. The experimental results of hot turning of stainless steel (type 316) were evaluated to ascertain the machining performance, such as (1) surface roughness (Ra), (2) tool life (T), and (3) metal removal rate (MRR). The surface roughness of the turned surface was measured using a Mitutoyo surfest SJ-201 contact profilometer. The instrument was set to a cutoff length of 0.8 mm with a transverse length of 5 mm. The tool life of the WC insert was examined by measuring the flank wear at regular intervals of time until flank wear reaches 0.2 mm and the total time of the turning process has been calculated [7]. This yields the tool life in terms of minutes. The flank wear of the WC insert was measured with the help of METZER tool maker microscope having a magnification factor of ×100. The metal removal rate was calculated by the following formula [8]: MRR ¼ Vc fs ap
ð1Þ
where MRR is the volume of metal removal rate (cm3/min), Vc is the cutting speed (m/min), fs is the feed rate (mm/rev), and ap is the depth of cut (mm). To determine the optimal machining parameters from the multiple performance characteristics, the orthogonal array with the grey relational analysis was used. The full factorial design would require 34 =81 experimental runs, which would make the effort and experimental cost prohibitive and unrealistic [9]. In this study, the Taguchi method, which is a powerful tool for parameter design of performance characteristics, was used. Total degree of freedom associated with four parameters was equal to 10 (4X1+2X3). Therefore, orthogonal array having a degree of freedom >10 should be selected. A standard L18 orthogonal array with 18 rows was used for the experiments. Four hot turning parameters were chosen as control factors, and each parameter was designed to have three levels, denoted 1, 2, and 3. The obtained values of surface roughness, tool life, and metal removal rate are presented in Table 3.
3 Grey relational analysis The Taguchi method is a systematic approach for design and analyzes the experiments to improve the product quality; it can simplify the optimization of process
Int J Adv Manuf Technol (2011) 56:455–462
457
a
i
d b
g
e
j
c f
k n m
o
l h
p
Fig. 1 Experimental setup of hot turning of stainless steel (type 316) workpiece. a Lathe head stock; b Three-jaw chuck; c Workpiece 316 SS; d Torch; e O2 flow control valve; f O2 cylinder; g LPG flow valve;
h LPG cylinder; i O2 pipe; j LPG pipe; k Temperature display; l Tail stock; m Thermocouple; n Wire; o Distance adjustment handle; p Cutting tool (WC insert)
parameters for multiple performance characteristics. To get optimized results, Taguchi recommends the use of the loss function to measure the performance characteristics deviated from the desired value [10]. In grey relational analysis, grey relational coefficients for different process characteristics were calculated and the average of these coefficients considered, which is called grey relational grade and was used as a single response for the Taguchi’s experimental plan; the same is illustrated in Fig. 2. A statistical ANOVA is performed to find out the statistical significance of the process parameters. With the grey relational analysis and statistical analysis of variance, the optimum combination of the process parameters can be predicted. Finally, a confirmation experiment is conducted to verify the optimal process parameters obtained from the process parameter design [11].
Hence, the S/N ratio of the two quality characteristics (higher-the-better type), tool life and metal removal rate, is computed using the following formula [12]: n P 1 1
3.1 S/N ratio to compute hot turning characteristics During hot turning of stainless steel (type316), the surface roughness of the turned parts has been considered as smaller-the-better type, whereas the tool life and metal removal rate are the higher-the-better types. These considerations have been made with respect to greater quality characteristics of interest.
Table 2 Hot turning parameters and their levels Level Cutting speed, Feed rate, fs Depth of cut, Workpiece (mm/rev) ap (mm) temperature (°C) Vc (m/min) 1 2 3
29.68 73.04 113.1
0.25 0.376 0.381
0.4 0.8 1
200 400 600
h¼10 log
n
S=N ratio
i¼1
yi 2
ð2Þ
in which yi is the evaluation indicator value of the tool life and metal removal rate measured to the ith time and n is the number of repeated experiment, in this case n=3. The surface roughness of the turned parts is termed as the smaller-the-better type problem where minimization of the characteristics is intended. Because of the surface roughness having smaller-the-better characteristics, column 2 in Table 4 shows the negative values and the S/N ratio calculated as follows [13]: n P 1 2 h¼10 log
S=N ratio
yi
n i¼1
ð3Þ
The experimental results of the surface roughness, tool life, metal removal, and S/N ratio are shown in Table 3. 3.2 Grey relational analysis for the S/N ratio In the grey relational analysis, data preprocessing is performed in order to normalize the raw data, and a linear normalization of the S/N ratio is performed [12]. The normalized S/N ratio means, when the range of the series is too large or the optimal value of a quality characteristic is too enormous, will cause the influence of some factors to be ignored, and the original experimental data must be normalized to eliminate such effect. Therefore, the normalized S/N ratio xij for the ίth performance characteristic in the jth
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Int J Adv Manuf Technol (2011) 56:455–462
Table 3 Experimental results for hot turning Cutting speed (m/min)
Feed rate (mm/rev)
Depth of cut (mm)
Workpiece temperature (°C)
Surface roughness, Ra (μm)
Tool life, T (min)
Metal removal rate, MRR (cm3/min)
1 2 3 4 5 6 7 8 9 10 11 12 13
29.68 29.68 29.68 73.04 73.04 73.04 113.1 113.1 113.1 29.68 29.68 29.68 73.04
0.25 0.376 0.381 0.25 0.376 0.381 0.25 0.376 0.381 0.25 0.376 0.381 0.25
0.4 0.8 1 0.8 1 0.4 0.4 0.8 1 1 0.4 0.8 1
200 400 600 600 200 400 600 200 400 200 400 600 400
4.2 4.4 2.8 4.2 4.2 2.7 4.4 3.9 2.8 3.8 4 2.8 2.8
26 34 30 22 35 32 27 38 31 20 31 29.5 24.5
2.968 8.927 11.308 7.304 21.97 27.81 11.31 34.02 43.09 1.468 8.927 11.3 7.3
14 15 16 17 18
73.04 73.04 113.1 113.1 113.1
0.376 0.381 0.25 0.376 0.381
0.4 0.8 0.8 1 0.4
600 200 400 600 200
4.2 4.7 3.9 2.4 4.4
32 29 23.5 34 29.5
21.97 27.82 11.31 34.02 43.09
Experiment no.
Experimentation based on the levels of the process parameters by Taguchi selection
experiments can be expressed in the following formula: xij ¼
Calculation of Normalized S/N ratio to the performance characteristics
Calculation of Grey relational co-efficient to each performance characteristics
Calculation of Grey relational Grade to each performance characteristics
Grey relational Grade analyzed by ANOVA from the experimental results.
Using the response table from the grey relation grade, selection of optimum parameter
Calculation of optimum level of grey relation grade
Comparison between experimental grey relation grade and estimated grey relation grade
Fig. 2 Flowchart of experiment
hij minj hij maxj hij minj hij
ð4Þ
where xij is the sequence after data processing, ηij the original sequence of S/N ratio (where i=1,2,3…m, j= 1,2,3…n), max ηij the largest value of ηij, and min ηij the smallest value of ηij Table 5 shows the normalized S/N ratio for surface roughness, tool life, and metal removal rate. Basically, the larger normalized S/N ratio corresponds to the better performance and the best normalized S/N ratio is equal to unity. The first row of Table 5 indicates the best normalized S/N ratios. The grey relational coefficient is calculated to express the relationship between the ideal (best) and actual normalized S/N ratio. The grey relational coefficient ξij for the ίth performance characteristic in the jth experiment can be expressed as follows: mini minj xi 0 xij þ z maxi maxj xi 0 xij xij ¼ xi 0 xij þ z maxi maxj xi 0 xij
ð5Þ
where x0i is the ideal normalized S/N ratio for the ίth performance characteristic and ζ the distinguishing coefficient which is in the range 0≤ζ≤1. A weighting method is then used to integrate the grey relational coefficients of each experiment into the grey relational grade. The overall evaluation of the multiple
Int J Adv Manuf Technol (2011) 56:455–462
459
Table 4 Multi-response S/N ratio for the hot turning performance Experiment no.
Surface roughness Ra
Tool life T
Metal removal rate MRR
1 2 3 4 5 6 7 8 9 10 11 12 13
−12.46 −12.87 −8.94 −12.46 −12.46 −8.63 −12.87 −11.82 −8.94 −11.59 −12.04 −8.94 −8.94
28.29 30.63 29.54 26.85 30.88 30.1 28.63 31.59 29.83 26.02 29.82 29.39 27.78
9.45 19.01 21.06 17.27 26.84 28.88 21.07 30.63 32.68 3.33 19.01 21.06 17.27
14 15 16 17 18
−12.46 −13.44 −11.82 −7.6 −12.87
30.1 29.25 27.42 30.63 29.39
26.84 28.89 21.06 30.63 32.66
performance characteristics is based on the grey relational grade, i.e., gj ¼
m 1 X wi xij : m i¼1
ð6Þ Table 6 Grey relational grade and its order
Table 5 Normalized S/N ratio of each individual quality Experiment no.
Assume that ω1 = ω2 = ω3 = 1, where γj is the grey relational grade for the jth experiment, ωi the weighting factor for the ith performance characteristic, and m the number of performance characteristics. Table 6 shows the grey relational grade for each experiment using the L18 orthogonal array. A higher grey relational grade indicates that the corresponding S/N ratio is closer to the normalized S/N ratio [13]. It has been shown that experiment 8 has the best multiple performance characteristics among the 18 experiment as it has the highest grey relational grade, as shown in Table 6. In other words, optimization of the complicated multiple performance characteristics can be converted into the optimization of a single grey relational grade. The effect of each hot turning process parameter on the grey relational grade at different levels can be independent because the experimental design is orthogonal. The mean of the grey relational grade for each level of the hot turning process parameters is summarized and shown in Table 7. In addition, the total mean of the grey relational grade for the 18 experiments is calculated and listed in Table 7. Figure 3 shows the percentage contribution of factors on the grey relational grade and Fig. 4 shows the hot turning parameters levels on the grey grade. Basically, the larger the grey relational grade, the better the multiple performance characteristics. However, the relative importance among
Surface roughness Ra
Tool life T
Metal removal rate MRR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0.832 0.902 0.23 0.832 0.832 0.176 0.902 0.723 0.23 0.6832 0.76 0.23 0.23 0.832 1
0.41 0.83 0.63 0.15 0.87 0.73 0.47 1 0.68 0 0.68 0.65 0.32 0.73 0.58
0.21 0.534 0.61 0.475 0.801 0.87 0.61 0.93 1 0 0.53 0.61 0.47 0.81 0.87
16 17 18
0.723 0 0.902
0.25 0.83 0.61
0.6 0.93 0.99
Experiment Grey relational coefficients no. Tool Metal removal Surface life (T) rate (MRR) roughness (Ra)
Grey grade
Order
1 2 3 4 5 6 7 8 9 10 11 12 13
0.748 0.836 0.394 0.748 0.748 0.38 0.84 0.644 0.394 0.612 0.68 0.393 0.393
0.46 0.75 0.57 0.37 0.79 0.65 0.48 1 0.61 0.33 0.61 0.59 0.42
0.388 0.518 0.562 0.488 0.715 0.794 0.56 0.877 1 0.333 0.515 0.562 0.485
0.532 0.701 0.508 0.535 0.751 0.608 0.627 0.84 0.668 0.426 0.602 0.515 0.433
13 6 15 12 4 10 8 1 7 17 11 14 16
14 15 16 17 18
0.75 1 0.643 0.33 0.84
0.65 0.54 0.4 0.75 0.561
0.725 0.794 0.555 0.877 0.998
0.708 0.778 0.533 0.625 0.799
5 3 12 9 2
Int J Adv Manuf Technol (2011) 56:455–462
0.7561 0.6876 0.6254 0.5318
3
0.6383 0.6783 0.6806 0.7319
D
0.523 0.555 0.611 0.6712
1
Cutting speed Feed rate Depth of cut Workpiece temperature
D
Level 3
3
Level 2
C
Level 1
C 1
A B C D
Process parameters
B3
Grey relational grade
A1
Symbol
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 B1
Table 7 Response table for the grey relational grade
A3
Grey Relational Grade
460
Process Parameter Level
Fig. 4 Effect of hot turning parameter levels on the grey grade
the hot turning process parameters for the multiple performance characteristics still needs to be known so that the optimal combination of the hot turning process parameter levels can be determined more accurately [14]. 3.3 Analysis of variance ANOVA is a standard statistical technique to interpret the experimental results. It is extensively used to identify the performance of a group of parameters under investigation. The purpose of ANOVA is to investigate the parameters whose combination to total variation is significant. In ANOVA, the total sum of squares deviations (SST) is calculated by: SST ¼
X
ðni mÞ2
ð7Þ
where m is the overall mean of the S/N ratio. The total sum of squared deviations, SST, is divided into two sources: np P
SST ¼
SSj þ SSe
i¼1
ð8Þ
where SSj is the sum of squared deviations for each design parameter and is given by l P
SSj ¼
i¼1
ðhji mÞ
where np is the number of significant parameters and l the number of levels of each parameter. SSe is the sum of squared error without or with pooled factor, which is the sum of squares corresponding to the insignificant factors. Mean square of a factor (MSj) or error (MSe) is found by dividing its sum of squares with its degree of freedom. Percentage contribution (ρ) of each of the design parameters is given by the following equation [15–17]: SSj
rj ¼ SST :
ð10Þ
ANOVA is successfully applied to investigate which hot turning process parameter significantly affects the performance characteristic. The ANOVA analysis in Table 8 and percentage contributions for each term affecting grey relational grade (Fig. 2) indicate that the cutting speed, feed rate, and temperature of the workpiece are the significant hot turning process parameters affecting the multiple performance characteristics. Furthermore, feed rate is the most significant process parameter due to its highest percentage contribution among the process parameters. Based on the above discussion, the optimal hot turning process parameters are cutting speed (113.1 m/min) at level 3, feed rate (0.381 mm/rev) at level 3, depth of cut (0.8 mm) at level 2, and workpiece temperature (400°C) at level 2. Therefore, experiment 8 shown in Table 2 fits the optimal process conditions.
2
ð9Þ
Fig. 3 Percentage contributions of factors on the grey relational grade
3.4 Confirmation tests The optimal hot turning process parameter revealed through the Taguchi orthogonal array from the experiments was A3B3C2D2 (113.1 m/min, 0.381 mm/rev, 0.8 mm, and 400°C), which was then employed to predict the grey relation that represents the hot turning of stainless steel (type 316) quality. ANOVA clearly indicates that A (cutting speed), B (feed rate), and D (temperature) can be classified as significant factors. Factor C (depth of cut) is excluded in the computation of the grey relation prediction value of the optimal hot turning parameter combination due to its minimal effect. Only the effects of more significant factors, i.e., A, B, and D, are taken into account for the prediction of
Int J Adv Manuf Technol (2011) 56:455–462 Table 8 ANOVA table
461
Source
df
Mean square
Sum of square
F value
% contribution
A B C D Error
2 2 2 2 9
0.029 0.06 0.011 0.019 0.0023
0.059 0.12 0.022 0.038 0.021
12.79 25.87 4.88 8.19
22.7 46.2 8.5 14.6 8
the grey relation αPredicted of the optimal hot turning parameters, and it can be expressed as: apredicted ¼ am þ
N X
ða 0 a m Þ
ð11Þ
i¼1
in which αPredicted is the grey relational grade for predicting the optimal hot turning parameters; α0 is the average grey relational grade of the optimal level of a certain significant factor—A, B, and D—αm the average grey relational grade; and N is the number of significant factors taken from ANOVA, which is 3. Factor C2 (depth of cut) is excluded in the prediction computation because factor C is insignificant. The effect of A3B3D2 is included. Computation of the grey relational grade for predicting the optimal hot turning parameters is as follows: apredicted ¼ am þ
3 X
ð12Þ
ða 0 a m Þ
i¼1
¼ 0:623 þ ð0:7561 0:623Þ þ ð0:6876 0:623Þ þð0:7319 0:623Þ¼ 0:9296
Finally, the confirmation experiment is conducted via the optimal hot turning parameter combination of A3B3C3D2 and was repeated three times. The evaluation points obtained for surface roughness, tool life, and metal removal rate are 3.6 μm, 37 min, and 43.100 cm3/min, respectively. The S/N ratio of the above three parameters were determined as −11.126, 31.36, and 32.69, respectively. The computational value of the grey relational grade is 0.9184. Table 9 Comparison between the initial and optimal process parameters
Level
Comparisons of the hot turning qualities between the initial hot turning parameter and the optimal hot turning parameter combination are shown in Table 9. It is found that utilization of the optimal hot turning parameter combination enhances the grey relation of single hot turning quality from 0.708 to 0.9184 by 29.7%.
4 Conclusion The following conclusions have been derived by applying the grey analysis on hot turning of stainless steel (type 316) ➢The experimental results clearly show that a cutting speed (Vc) at 113.1 m/min, feed rate (fs) at 0.381 mm/rev, and workpiece temperature at 400°C will give the optimum results for hot turning of stainless steel (type 316) by employing multi-response optimization using grey relational analysis. ➢Based on the Taguchi method and ANOVA, feed rate has a dominant effect of almost 46.2% in contribution ratio, while cutting speed has 22.7% and workpiece temperature has 14.6% influence on the surface roughness, tool life, and metal removal rate in hot turning of stainless steel (type 316). The machining parameters set at their optimum levels can ensure significant improvement in the process parameters. ➢The cutting speed, feed rate, and workpiece temperature are primary factors that affect the quality of hot turning of stainless steel (type 316), while the depth of cut is considered a secondary factor. ➢Significant improvement in grey relation can be achieved by a combination of optimal hot turning parameters. Raw
Optimal hot turning parameters
A2B2C1D2
Prediction A3B3C2D2
Experiment A3B3C2D2
0.9296
3.6 37 43.66 0.9184
Surface roughness (μm) 4.2 Tool life (min) 32 Metal removal rate (cm3/min) 21.97 Grey relational grade 0.708 Percentage of improvement of the grey relational grade=29.7%
462
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