Influence of slab milling process parameters on surface integrity of ...

Int J Adv Manuf Technol (2012) 61:859–871 DOI 10.1007/s00170-011-3763-y

ORIGINAL ARTICLE

Influence of slab milling process parameters on surface integrity of HSLA: a multi-performance characteristics optimization Pankul Goel & Zahid A. Khan & Arshad Noor Siddiquee & Shahrul Kamaruddin & Rajeev Kumar Gupta

Received: 29 April 2011 / Accepted: 7 November 2011 / Published online: 22 November 2011 # Springer-Verlag London Limited 2011

Abstract An attempt has been made in this paper to determine the optimal setting of slab milling process parameters. Four process parameters, i.e. cutting fluid, cutting speed, feed and depth-of-cut each at three levels except the cutting fluid at two levels, were considered. The multiperformance characteristics of the process were measured in terms of surface integrity defined by surface roughness, surface strain and micro-hardness of the work-piece. Eighteen experiments, as per Taguchi’s L18 orthogonal array, were performed on high-strength low-alloy steel. Grey relational analysis, being a widely used technique for multi-performance optimization, was used to determine Grey relational grade. Subsequently, Taguchi response table method and ANOVA were used for data analysis. Confirmation experiment was conducted to determine the improvement in the surface integrity using this approach. Results revealed that machining done in the presence of P. Goel Department of Mechanical Engineering, Vidya College of Engineering, Baghpat Road, Meerut, Uttar Pradesh, India Z. A. Khan : A. N. Siddiquee (*) Department of Mechanical Engineering, Jamia Millia Islamia (A Central University), New Delhi, India e-mail: [email protected] S. Kamaruddin School of Mechanical Engineering, University Science Malaysia, Nibong Tebal, Penang, Malaysia R. K. Gupta Delhi Institute of Tool Engineering, Okhla, New Delhi, India

cutting fluid, at a cutting speed of 1,800 r.p.m. with a feed of 150 mm/min and depth-of-cut of 0.23 mm, yielded the optimum multi-performance characteristics of the slab milling process. Further, the results of ANOVA indicated that all four machining parameters significantly affected the multi-performance with maximum contribution from depth-of-cut (33.76%) followed by feed (24.02%), cutting speed (16.29%) and cutting fluid (13.21%). Keywords Slab milling . Surface integrity . Grey relational grade . Multi-performance . Optimization

1 Introduction Milling machine is a versatile machine tool and it is used to machine many industrial components such as those used in construction and farm machineries, mining and rail road cars, various types of commercial and passenger vehicles, earthmover barge and barrages. Most of these components are produced from HSLA steels. The reliability of these components to perform intended functions when put to service depends to a greater extent on the machined components’ surface integrity (SI). SI is concerned with the quality and condition of the surface and subsurface of the machined components. Field and Kahles [1] have defined SI as the relationship between the physical properties and the functional behaviour of a surface. SI is determined by the geometrical values of the surface such as surface roughness (for example, Ra and Rt), the physical properties such as residual stresses, hardness and structure of the surface layers. In order to maintain a high production rate with an acceptable quality level of the machined parts, it is important to select the optimum combination of

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machining parameters such as feed, cutting fluid, cutting speed and depth-of-cut as these parameters have impact on the multi-performance characteristics of the process like surface roughness, strain hardening, micro-hardness and microstructure which are indeed constituents of SI. Literature reveals that there has been growing interest amongst researchers to explore various aspects of SI of the machined components. Sun and Guo [2] conducted a series of end milling experiments to comprehensively characterize SI of Ti–6Al–4 V and concluded that (1) surface roughness value increased with feed and radial depth-of-cut but had much less variation in the selected cutting speed range, (2) compressive residual normal stresses occurred in both cutting and feed directions, while the influences of cutting speed and feed on residual stress trend were quite different and (3) the milled surface micro-hardness was about 70– 90% higher than the bulk material in the subsurface. Gallab and Sklad [3] studied the SI of Al–20% SiC particulate metal–matrix composites (PMMC) machined by polycrystalline diamond tools and observed that machining of PMMC is most economical and safe at a speed of 894 m min−1, a depth-of-cut of 1.5 mm and feed rates as high as 0.45 mm rev−1 for which surface roughness Rmax should not exceed 2.5 μm. Khabeery et al. [4] studied the effect of milling roller–burnishing parameters on SI of 6061-T6 aluminium alloy and observed that the optimum number of passes should be three or four with maximum burnishing speed 120 m/min to obtain high surface quality. Haron [5] reported that the surface of titanium alloy Ti-6246 (Ti–6Al– 2Sn–4Zr–6Mo) is easily damaged due to poor machinability and also observed severe plastic deformation and hardening after prolonged machining time with worn tools, especially under dry cutting condition. Axinte and Dewes [6] conducted SI study on hardened AISI H13 hot work tool steel by using solid carbide ball nose-end mills coated with TiAlN on high speed milling and concluded that (1) Ra values increased when cutting speed increased and feed per tooth decreased due to higher process instability. However, Ra values decreased with 60° work-piece angle due to absence of the rubbing effect caused by the centre of the tool at 0° for a measured range of Ra 0.36–2.18 μm; (2) No significant white layers or other heat-affected zones were found below the machined surface in all the tests and none of the variable parameters affected micro-hardness significantly; (3) Compressive stress decreased for two reasons: one higher work-piece angle due to absence of the rubbing (mechanical) effect caused by the centre of ball noseend mill and second for increasing cutting speed and feed per tooth due to an increase in thermal effect on the machined surface. Dhar et al. [7] found significant improvement in the surface finish and dimensional accuracy under cryogenic cooling. Novovic et al. [8] performed conventional and non-conventional machining

Int J Adv Manuf Technol (2012) 61:859–871

processes on a variety of work-piece materials and observed that machined surface roughness in excess of 0.1 μm Ra has a strong influence on fatigue life in the absence of residual stress, but for the range 2.5–5 μm Ra, fatigue life is primarily dependent on work-piece residual stress and surface microstructure. They, however, observed that the presence of inclusions larger in size than the machined surface roughness generally overrides the effect of surface topography. Dhar et al. [9] reported that surface roughness of AISI-4340 steel was significantly reduced when turning was performed with minimum quantity lubrication. Basavarajappa et al. [10] conducted drilling tests on Al2219–15% SiCp and Al2219–15% SiCp–3% graphite (hybrid) composites and found that the surface roughness decreased with the increase in cutting speed but increased with the increase in feed rate. They also observed that subsurface deformation extends up to a maximum of 120 μm below the machined surface for Al2219/15% SiCp–3% Gr composite when compared to 150 μm in Al2219/15% SiCp composite. Javidi et al. [11] conducted an experimental study on 34CrNiMo6 where the effect of turning showed that the residual stress on fatigue life is more pronounced than the effect of surface roughness. They also observed that plastic deformation of the grain boundaries was found at the first 3–4 μm of the subsurface layer after machining. Keeping in view the wide range of application of HSLA milled components, an attempt has been made in this study to optimize milling parameters such as feed, cutting fluid, cutting speed and depth-of-cut for multi-performance characteristics of the slab milling process defined by the work-piece surface roughness, surface strain and micro-hardness. Taguchi-based Grey relational analysis is used to determine Grey relational grade which reflects multi-performance characteristics of milling operation. Analysis of variance (ANOVA) is employed to determine the machining parameters that significantly affect the multi-performance and also the percentage contribution of these parameters. Finally, the confirmation test is carried out to validate the results of the present study.

2 Experiments 2.1 Material, test condition and measurement K-series solid carbide tool (Fig. 1) was used to perform slab down milling operation. The cutting tool specifications were as follows: type—ball nose tool, length=150 mm, number of cutting teeth=04 and helix angle=30°. ASTM A572-grade 50 HSLA plates 700×400×100 mm in size were used as work-piece material. The chemical composition of the workpiece material is shown in Table 1.


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Fig. 1 Carbide tool

The experimental studies were performed on a CNC milling machine (make: MCM, Italy; model: connection; tool movement: X—600 mm, Y—680 mm, Z—650 mm and 22 kW/3,500 rpm). The experimental setup is shown in Fig. 2. A water-soluble mineral oil (trade name: BD68, origin: Indian Oil Corporation Limited, India) was used as cutting fluid. Different settings of cutting fluid, cutting speed, feed and depth-of-cut were used in the experiments (Table 2). The surface roughness of all the specimens was measured using the Taylor–Hobson SurfCom instrument (Fig. 3) for a sampling length of 5 mm as per the recommendations of ASME B-46.1-2002. In order to measure surface strain of the machined surface, square grid of size 1×1 mm was formed on the entire surface of the specimen with the help of a pantograph before machining. During machining, the grids were plastically deformed due to cutting force exerted by the tool. The amount of deformation was measured using Mitutoyo profile projector (×10) (Fig. 4) at three different points on the machined surface. Out of three values of the measured deformation, the maximum value was considered which represented the surface strain. Prior to the measurement of micro-hardness, the metallographic finish of each specimen was done on a disc polishing machine by using abrasive (grit sizes 200, 300, 500, 800, 1,000, 1,500 and 2,000, respectively) and Al2O3 (grades III, II and I, respectively). Subsequently, micro-hardness of specimens was measured by using Mitutoyo micro-hardness tester (Fig. 5). After performing machining operations on the specimens, the micrograph of each specimen in the region of maximum shear strain was also obtained to study the surface microstructure. The micrographs were taken at ×400

Table 1 Material composition Element

Concentration (% by weight)

Element

Concentration (% by weight)

Iron Carbon Silicon Manganese Sulphur Phosphorous Nickel Chromium Molybdenum

98.31 0.187 0.039 1.35 0.025 0.027 0.012 0.010 0.014

Aluminium Copper Tin Niobium Cobalt Boron Lead Vanadium Zirconium

0.004 0.011 0.000 0.001 0.002 0.000 0.001 0.001 0.001

Fig. 2 Experimental setup

magnification using a Rsamet Unitrom optical microscope. The micrographs of the specimens for the 18 experiments are shown in Fig. 6. 2.2 Design of experiment based on Taguchi method The experiments were conducted based on Taguchi’s experimental design for which an appropriate orthogonal array was selected. To select an appropriate orthogonal array for the experiments, the total degrees of freedom are computed. The degrees of freedom are defined as the number of comparisons between design parameters that need to be made to determine which level is better and specifically how much better it is. For example, a threelevel design parameter counts for two degrees of freedom. The degrees of freedom associated with the interaction between two design parameters are given by the product of the degrees of freedom for the two design parameters. Therefore, there are seven degrees of freedom owing to there being four machining parameters in the slab downmilling operation. Once the required degrees of freedom are Table 2 Machining settings used in the experiments Factor identifier

Factor

Unit

Level 1

Level 2

Level 3

A B C D

Cutting fluid Cutting speed Feed Depth of cut

– r.p.m. mm/min mm

Absent 1,800 150 0.13

Present 2,300 225 0.18

– 2,800 300 0.23

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Fig. 3 Taylor–Hobson SurfCom instrument

known, the next step is to select an appropriate orthogonal array to fit the specific task. Basically, the degrees of freedom for the orthogonal array should be greater than or at least equal to those for the design parameters. In this study, an L18 orthogonal array with 18 rows (corresponding to the number of experiments) was chosen for the experiments (Table 3). L18 array has a special property that the two-way interactions between the various parameters are partially confounded with various columns and hence their effect on the assessment of the main effects of the various parameters is minimized. With L18 array, the main effects of different process parameters can be assessed with reasonable accuracy. According to the scheme of the experimentation outlined in L18 OA (Table 3), slab milling operations were performed on HSLA work-piece.


Fig. 5 Mitutoyo micro-hardness tester

3 Analysis method 3.1 Signal-to-noise ratio Taguchi method is one of the simplest and effective approaches for parameter design and experimental planning [12]. In this method, the term ‘signal’ represents the desirable value (mean) for the output characteristic and the term ‘noise’ represents the undesirable value (S.D.) for the output characteristic. Therefore, the S/N ratio is the ratio of the mean to the S.D. There are three types of S/N ratio depending on the type of characteristics—the lower the better, the higher the better, and the nominal the better. The S/N ratio with a “the lower the better” characteristic can be expressed as [13]: ! n 1X 2 hij ¼ 10 log y ð1Þ n j¼1 ij The S/N ratio with a “the nominal the better” characteristic can be expressed as [13]: ! n 1 X 2 y hij ¼ 10 log ð2Þ ns j¼1 ij The S/N ratio with a “the higher the better” characteristic can be expressed as [13]: ! n 1X 1 hij ¼ 10 log ð3Þ n j¼1 y2ij where yij is the ith experiment at the jth test, n is the total number of the tests and s is the standard deviation. Regardless of category of the performance characteristics, a greater η value corresponds to a better performance. 3.2 Data pre-processing

Fig. 4 Mitutoyo profile projector

In Grey relational analysis, the function of factors is neglected in situations where the range of the sequence is large or the standard value is enormous. However, this


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Fig. 6 Micrographs of the specimens: a Experiment no. 1. b Experiment no. 2. c Experiment no. 3. d Experiment no. 4. e Experiment no. 5. f Experiment no. 6. g Experiment no. 7. h Experiment no. 8. i Experiment no. 9. j Experiment no. 10. k

Experiment no. 11. l Experiment no. 12. m Experiment no. 13. n Experiment no. 14. o Experiment no. 15. p Experiment no. 16. q Experiment no. 17. r Experiment no. 18

analysis might produce incorrect results if the factors, goals and directions are different. Therefore, one has to pre-process the data which are related to a group of sequences, which is called “Grey relational generation” [13]. Data preprocessing is a process of transferring the original sequence to a comparable sequence. For this purpose, the experimental results are normalized in the range between zero and one. The normalization can be done from three different approaches [14]. If the target value of original sequence is infinite, then it has a characteristic of “the larger the better”. The original sequence can be normalized as follows [14]:

If the expectancy is “the smaller the better”, then the original sequence should be normalized as follows:

»

xi ðkÞ ¼

»

xi ðkÞ ¼

max x0i ðkÞ x0i ðkÞ max x0i ðkÞ min x0i ðkÞ

ð5Þ

However, if there is a definite target value to be achieved, the original sequence will be normalized in the form: 0 x ðkÞ x0 i xi ðkÞ ¼ 1 max x0i ðkÞ x0 »

ð6Þ

»

xi ðkÞ min x0i ðkÞ max x0i ðkÞ min x0i ðkÞ

ð4Þ

or the original sequence can be simply normalized by the most basic methodology, i.e. let the values of

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Table 3 Experimental design using L18 orthogonal

Experiment no.

A

B

C

D

Surface roughness, Ra (μm)

Max surface strain

Micro-hardness HV

1

1

1

1

1

0.2427

0.3180

162

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2

1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

2 3 1 2 3 2 3 1 3 1 2 2 3 1 3 1 2

0.2419 0.2538 0.2224 0.2212 0.2889 0.2948 0.2413 0.2638 0.3121 0.2337 0.2697 0.2365 0.261 0.2635 0.2775 0.2434 0.2998

0.3230 0.3895 0.3120 0.2970 0.2820 0.2830 0.3000 0.3110 0.2980 0.3180 0.2670 0.2720 0.2520 0.2460 0.2470 0.2450 0.2730

163 160 161 153 163 167 165 166 176 169 174 179 173 161 179 168 167

original sequence be divided by the first value of sequence: »

xi ðkÞ ¼

x0i ðkÞ x0i ð1Þ

ð7Þ

" is distinguishing or identification coefficient: " ∈[0,1], " = 0.5 is generally used. After obtaining the Grey relational coefficient, its average is calculated to obtain the Grey relational grade. The Grey relational grade is defined as follows14:

»

where xi ðkÞ is the value after Grey relation generation (data pre-processing), max x0i ðkÞ is the largest value ofx0i ðkÞ, min x0i ðkÞ is the smallest value of x0i ðkÞ and x0 is the desired value. 3.3 Grey relational coefficient and Grey relational grade Following data pre-processing, a Grey relational coefficient is calculated to express the relationship between the ideal and actual normalized experimental results. The Grey relational coefficient can be expressed as follows [14]: xi ðkÞ ¼

Δmin þ " : Δmax Δ0i ðkÞ þ " : Δmax

ð8Þ

where Δ0i(k) is the deviation sequence of the reference » » sequence x0 ðkÞ and the comparability sequence xi ðkÞ, namely, » » Δ0i ðkÞ ¼ x0 ðkÞ xi ðkÞ; » » Δmax ¼ max maxx0 ðkÞ xj ðkÞ; 8j"i 8k » » Δmin ¼ min minx0 ðkÞ xj ðkÞ; 8j"i

8k

gi ¼

n 1X x ðkÞ n k¼1 i

ð9Þ

However, since in real application the effect of each factor on the system is not exactly same, Eq. 9 can be modified as: gi ¼

n X k¼1

wk :xi ðkÞ

n X

wk ¼ 1

ð10Þ

k¼1

where wk represents the normalized weighting value of factor k. Given the same weights, Eqs. 9 and 10 are equal. In Grey relational analysis, the Grey relational grade is used to show the relationship among the sequences. If the two sequences are identical, then the value of Grey relational grade is equal to 1. The Grey relational grade also indicates the degree of influence that the comparability sequence could exert over the reference sequence. Therefore, if a particular comparability sequence is more important than the other comparability sequence to the reference sequence, then the Grey relational grade for that comparability sequence and reference sequence will be higher than other Grey relational grades [15]. In this study, the importance of both the comparability sequence and reference sequence is treated as equal.


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4 Analysis and discussion of experimental results

Table 5 The sequences of each performance characteristic after data pre-processing

4.1 Optimal parameter combination

Experiment no.

Surface roughness

Surface strain

Micro-hardness

Reference sequence 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1.0000 0.2694 0.2599 0.3994 0.0157 0.0000 0.7756 0.8343 0.2526 0.5116 1.0000 0.1597 0.5759 0.1943 0.4806 0.5083 0.6587 0.2778

1.0000 0.5625 0.5962 1.0000 0.5214 0.4152 0.3034 0.3110 0.4368 0.5145 0.4224 0.5625 0.1855 0.2255 0.0608 0.0088 0.0175 0.0000

1.0000 0.3642 0.4034 0.2850 0.3247 0.0000 0.4034 0.5579 0.4811 0.5196 0.8923 0.6337 0.8195 1.0000 0.7828 0.3247 1.0000 0.5959

18

0.8832

0.2334

0.5579

The work-piece surface roughness, surface strain and micro-hardness for different combinations of milling parameters of 18 experimental runs are listed in Table 3. The following sequential steps were adopted to determine the optimal combination of the slab milling process parameters based on Grey relational analysis: 1. 2. 3. 4. 5.

S/N ratios for the experimental data were calculated. The S/N ratios were normalized. Corresponding Grey relational coefficients were calculated. The Grey relational grades were calculated. ANOVA was carried out to determine the significant contribution of the factors. 6. Confirmation test was carried out to check the validity of the results.

As far as SI is concerned, the lower surface roughness, the lower surface strain and the lower micro-hardness are indications of better performance. The S/N ratios of the work-piece surface roughness, surface strain and microhardness for 18 experimental runs calculated using Eq. 1 are listed in Table 4. The values of surface roughness, surface strain and micro» hardness are set to be the reference x0 ðkÞ, k=1 - 3. Moreover; Table 4 The S/N ratio

the results of 18 experiments were the comparability » sequences xi ðkÞ , i=1,2,3…18, k=1 - 3. Table 5 lists all of the sequences following data pre-processing of surface roughness, surface strain and micro-hardness by using Eq. 5.

Experiment no. A B C D S/N ratio (dB) Table 6 The deviation sequences Surface Max surface Microroughness strain hardness 1 2 3 4 5 6 7 8

1 1 1 1 1 1 1 1

1 1 1 2 2 2 3 3

1 2 3 1 2 3 1 2

1 2 3 1 2 3 2 3

12.30 12.33 11.91 13.06 13.10 10.79 10.61 12.35

9.95 9.82 8.19 10.12 10.54 11.00 10.96 10.46

−44.19 −44.24 −44.08 −44.14 −43.69 −44.24 −44.45 −44.35

9 10 11 12 13 14 15 16 17 18

1 2 2 2 2 2 2 2 2 2

3 1 1 1 2 2 2 3 3 3

3 1 2 3 1 2 3 1 2 3

1 3 1 2 2 3 1 3 1 2

11.57 10.11 12.63 11.38 12.52 11.67 11.58 11.13 12.27 10.46

10.14 10.52 9.95 11.47 11.31 11.97 12.18 12.15 12.22 11.28

−44.40 −44.91 −44.56 −44.81 −45.06 −44.76 −44.14 −45.06 −44.51 −44.45

Deviation sequences

Δ0i(1)

Δ0i(2)

Δ0i(3)

Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment Experiment

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

0.7306 0.7401 0.6006 0.9843 1.0000 0.2244 0.1657 0.7474 0.4884 0.0000 0.8403 0.4241 0.8057 0.5194 0.4917 0.3413

0.4375 0.4038 0.0000 0.4786 0.5848 0.6966 0.6890 0.5632 0.4855 0.5776 0.4375 0.8145 0.7745 0.9392 0.9912 0.9825

0.6358 0.5966 0.7150 0.6753 1.0000 0.5966 0.4421 0.5189 0.4804 0.1077 0.3663 0.1805 0.0000 0.2172 0.6753 0.0000

Experiment no. 17 Experiment no. 18

0.7222 0.1168

1.0000 0.7666

0.4041 0.4421

no. no. no. no. no. no. no. no. no. no. no. no. no. no. no. no.

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Table 7 The calculated Grey relational coefficient and Grey relational grade and its orders for 18 comparability sequences Experiment Grey relational coefficients Grey Orders no. relational Surface Maximum Micro- grade roughness surface strain hardness 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.4063 0.4032 0.4543 0.3369 0.3333 0.6902 0.7511 0.4008 0.5059 1.0000 0.3730 0.5410 0.3829 0.4905 0.5042

0.5334 0.5532 1.0000 0.3917 0.4609 0.4178 0.4205 0.4703 0.5074 0.4640 0.5334 0.3804 0.3923 0.3474 0.3353

0.4402 0.4560 0.4115 0.4254 0.3333 0.4560 0.5307 0.4907 0.5100 0.8228 0.5772 0.7347 1.0000 0.6971 0.4254

0.4600 0.4708 0.6219 0.3847 0.3759 0.5213 0.5675 0.4540 0.5077 0.7623 0.4945 0.5521 0.5917 0.5117 0.4216

13 12 3 17 18 8 6 14 10 1 11 7 4 9 16

16 17 18

0.5943 0.4091 0.8106

0.3373 0.3333 0.3948

1.0000 0.5530 0.5307

0.6439 0.4318 0.5787

2 15 5

Also, the deviation sequences Δ0i, Δmax(k) and Δmin(k) for i=1–18 and k=1–3 can be calculated as follows: » » Δ01 ð1Þ ¼ x0 ð1Þ x1 ð1Þ ¼ j1:0000 0:2694j ¼ 0:7306 » » Δ01 ð2Þ ¼ x0 ð2Þ x1 ð2Þ ¼ j1:0000 0:5625j ¼ 0:4375 » » Δ01 ð3Þ ¼ x0 ð3Þ x1 ð3Þ ¼ j1:0000 0:3642j ¼ 0:6358 So, Δ01(1)=(0.7306,0.4375,0.6358)

Fig. 7 Graph of Grey relational grade

Table 8 Response table for Grey relational grade Symbol Milling machining Level 1 Level 2 Level 3 Max–min parameter A B C D


0.4849 0.5603 0.5683 0.4501

0.5543 0.4678 0.4564 0.5228

– 0.5306 0.5339 0.5858

0.0694 0.0924 0.1119 0.1358

The results of all Δ 0i for i = 1–18 are given in Table 6. Using Table 6, Δ max and Δmin can be found as follows: Δmax ¼ Δ05 ð1Þ ¼ Δ17 ð2Þ ¼ Δ05 ð3Þ ¼ 1:0000 Δmin ¼ Δ10 ð1Þ ¼ Δ03 ð2Þ ¼ Δ13 ð3Þ ¼ 0:0000 The distinguishing coefficient ζ can be substituted for the Grey relational coefficient in Eq. 8. If all the process parameters have equal weighting, ζ is 0.5. Table 7 lists the Grey relational coefficient and grade for each experiment of the L18 orthogonal array by applying Eqs. 8 and 10. According to performed experiment design, it is clearly observed from Table 7 and the Grey relational grade graph (Fig. 7) which shows the change in the response when the factors go from one level to other that the milling parameters’ setting of experiment no. 10 has the highest Grey relation grade. Thus, the tenth experiment gives the best multi-performance characteristics of the slab milling process among the 18 experiments. The response table of Taguchi method was employed to calculate the average Grey relational grade for each factor level. The procedure was to group the relational

Fig. 8 Effect of milling parameter levels on multi-performance

Int J Adv Manuf Technol (2012) 61:859–871 Table 9 Interaction response for A×B

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A×B

A—L1

A—L2

B—L1 B—L2 B—L3

0.5176 0.4273 0.5097

0.6029 0.5083 0.5514

grades firstly by factor level for each column in the orthogonal array and then to average them. For instance,

Table 11 Interaction response for A×D

A×D

A—L1

A—L2

D—L1 D—L2 D—L3

0.4508 0.4714 0.5324

0.5742 0.4793 0.6393

the Grey relational grade for factor A at level 1 and level 2 can be calculated as follows:

A1 ¼ ð0:4600 þ 0:4708 þ 0:6219 þ 0:3847 þ 0:3759 þ 0:5213 þ 0:5675 þ 0:4540 þ 0:5077Þ=9 ¼ 0:4849 A2 ¼ ð0:7623 þ 0:4945 þ 0:5521 þ 0:5917 þ 0:5117 þ 0:4216 þ 0:6439 þ 0:4318 þ 0:5787Þ=9 ¼ 0:5543

Using the same method, calculations were performed for each milling parameter level and the response table was constructed as shown in Table 8. Since the Grey relational grades represented the level of correlation between the reference and the comparability sequences, the larger Grey relational grade means the comparability sequence exhibits a stronger correlation with the reference sequence. Therefore, the comparability sequence has a larger value of Grey relational grade for the surface roughness, surface strain and micro-hardness. Based on this hypothesis, this study selects the level that provides the largest average response. Figure 8 is the response graph for the milling parameters and shows the mean value of Grey relational grade at different levels of each milling parameter. The dashed line in this figure is the value of the total mean of the Grey relational grade. Basically, the larger the Grey relational grade, the better are the multi-performance characteristics. From Table 8 and Fig. 8, it can be seen that A2, B1, C1 and D3 show the largest value of Grey relational grade for factors A, B, C and D, respectively. Therefore, A2B1C1D3 is the condition for the optimal parameter combination for the multi-performance characteristic of the slab milling process. The optimum cutting condition obtained in the present study that yields the smallest surface roughness, smallest surface strain and smallest micro-hardness appears to be quite logical. In the presence of cutting fluid, the effective friction is low, which in turn increases the shear angle. The increase in shear angle reduces the shear strain. Further, as the down milling operation was performed with a helical slab milling cutter, the chip thickness as well as the cutting forces per teeth increased gradually and the surface

roughness obtained was less. When cutting speed and feed at low level, i.e. level one, are used, then the feed per tooth is small and this condition leads to reduced surface finish and reduced surface residual strain. When the last column of Table 8 was compared, it is observed that the difference between the maximum and minimum value of the Grey relational grade for factor D is the largest followed by factors C, B and A. This indicates that the depth-of-cut has a stronger effect on the multiperformance characteristics followed by feed, cutting speed and cutting fluid. To estimate parameter interaction effects, interaction tables were used [16]. Tables 9, 10, 11, 12, 13 and 14 display the interaction response for A×B, A×C, A×D, B×C, B×D and C×D, respectively. Here A–L1 denotes factor A at level 1, with similar nomenclature for the others. The data shown in the interaction response tables are calculated from Table 7. As an example, the data under A– L1 and B–L1 in Table 9 are the averages of the results of experiment trials 1–3 where A was at level 1 and, at the same time, B was also at level 1. The data in the other positions of the table were determined using the same procedure [17]. These tables are also shown in graphical form in Figs. 9, 10, 11, 12, 13 and 14, respectively. If the lines on the interaction plots are nonparallel interactions occur, and if the lines cross strong interactions occur between factors [16]. It can be seen that Figs. 9 and 11 reveal no interaction, while Figs. 10, 12, 13 and 14 exhibit a strong interaction, respectively. Because the interaction response for A×B as well as A×D is not obvious, these interactions were not considered in the present study. Table 12 Interaction response for B×C

Table 10 Interaction response for A×C

A×C

A—L1

A—L2

B×C

B—L1

B—L2

B—L3

C—L1 C—L2 C—L3

0.4707 0.4336 0.5503

0.6659 0.4793 0.5175

C—L1 C—L2 C—L3

0.6112 0.4827 0.5870

0.4882 0.4438 0.4715

0.6057 0.4429 0.5432

868


Table 13 Interaction response for B×D B×D

B—L1

B—L2

B—L3


0.4773 0.5115 0.6921

0.4032 0.4838 0.5165

0.4698 0.5731 0.5489

Figure 10 reveals that when machining is done in the presence of cutting fluid at low speed, then the multiperformance, i.e. the Grey relational grade, is maximum. Figure 10 further shows that, on increase of speed, the Grey relational grade drops at intermediate level of speed and subsequently stabilizes. When the machining is performed in the absence of cutting fluid, the grade is lower at lower speed but it increases slightly when speed is increased. It is also interesting to observe that the multi-performance characteristic of the process is marginally affected by the cutting speed when the machining is performed in the absence of cutting fluid (Fig. 10). At lower cutting speed, the cutting fluid plays a predominant role as a lubricant and it leads to better surface finish, which is one of the response variables, while on increasing cutting speed the cutting forces are lower and the rise in cutting temperature is contained by the cooling action, causing the strain hardening and residual strain to increase. This results in the drop in Grey relational grade. On further increase in cutting speed from intermediate level to higher level, the temperature rises as the response time for the cutting fluid decreases. The lower machining forces and higher temperature at higher cutting speed result in lower strain and residual stress but higher surface finish, and thus the Grey relational grade is marginally improved. During machining in the absence of cutting fluid, friction is higher and the increase in cutting speed reduces force and improves the surface finish, while the rise in temperature further increases the friction. The effect of temperature rise and cutting speed mutually contradict each other and the effect of increase in cutting speed affects the Grey relational grade marginally. Figure 12 depicts that the interaction between cutting speed and feed has a strong effect on the Grey relational grade. It shows that machining at high speed with low as well as intermediate feed (translated into 53 and 80 μm per revolution, respectively) causes low surface roughness and

Fig. 9 The plot of interaction response for A×B

also low strain (due to higher machining temperature). Thus, at higher levels of speed, highest Grey grade is observed when the feed is low. It is only expected that under these machining conditions the surface finish and machining forces (and hence the residual stress) both cause the grade to increase. At intermediate speed, however, the unstable built-up-edge condition prevails, causing the Grey grade to be kept low, and for all levels of feed at intermediate speed the Grey grade does not change much and remains lowest. Figure 13 exhibits a strong interaction effect of cutting speed and depth-of-cut on the Grey relational grade. The increase in surface residual strain and surface roughness during increase in the depth coupled with the higher levels of machining speed gets compounded and consequently the Grey grade reduces. Machining at lower speed and at higher values of the depth-of-cut causes increase in shear angle, which consequently reduces the residual stresses. Thus, an improvement in Grey relational grade is seen on increase in depth-of-cut when machining is done at lower levels of speed. Figure 14 displays a strong interaction effect of feed and depth-of-cut on the Grey relational grade. The grade remains virtually unaffected with respect to increase in depth-of-cut at intermediate feed. At lower feed, a continuous increase in grade is seen as depth-of-cut is increased. The increase in grade is, however, marginal at higher feed levels. The smaller feed gives better surface finish but an increase in depth-of-cut, keeping the feed

Table 14 Interaction response for C×D C×D

C—L1

C—L2

C—L3


0.4224 0.5796 0.7031

0.4632 0.4234 0.4829

0.4647 0.5654 0.5716

Fig. 10 The plot of interaction response for A×C


869

Fig. 13 The plot of interaction response for B×D Fig. 11 The plot of interaction response for A×D

constant, increases the shear angle. Thus, within the chosen range of depth-of-cut, an increase in shear angles positively affects the strain-hardening condition at the machined surface. Thus, in case of a smaller level of feed, an increase in depth-of-cut causes the grade to increase steeply. The grade, however, marginally improves in case of higher levels of feed and it remains virtually unaffected at intermediate feed level. 4.2 Analysis of variance The purpose of ANOVA was to investigate which slab milling parameters significantly affected the multiperformance characteristics. This was accomplished by separating the total variability of the Grey relational grades, which was measured by the sum of the square deviations from the total mean of the Grey relational grade, into contributions by each slab milling process parameter and the error. The percentage contribution by each of the

Fig. 12 The plot of interaction response for B×C

process parameter in the total sum of the squared deviations was used to evaluate the importance of the process parameter change on the performance characteristic. In addition, the F test was also used to determine which slab milling process parameters had a significant effect on performance characteristic. Usually, the change of the slab milling process parameter has a significant effect on performance characteristic when the F value is large. Table 15 shows that F value is largest for parameter depth-of-cut. The results of ANOVA for the Grey relational grade are listed below in Table 15. Table 15 shows that all four machining parameters significantly affected the multi-performance characteristics of the slab milling process. The results of ANOVA also reveal that the depth-of-cut is the most significant slab milling process parameter affecting the multi-performance characteristics due to its highest percentage contribution

Fig. 14 The plot of interaction response for C×D

870


Table 15 Results of the analysis of variance Symbol

Machining parameters

Degrees of freedom

Sum of square

Mean square

F ratio

P-value

Contribution (%)

A B C D Error Total


1 2 2 2 10 17

0.0217 0.0267 0.0394 0.0554 0.0209 0.1641

0.0217 0.0134 0.0197 0.0277 0.0021

10.38 6.40 9.43 13.26

0.0091 0.0162 0.005 0.0015

13.21 16.29 24.02 33.76 12.73 100.00

amongst the selected process parameters. Table 9 further shows that the percentage contribution of other parameters in decreasing order is feed (24.01%), cutting speed (16.29%) and cutting fluid (13.21%). The micrographs shown in Fig. 6 suggest that in all cases the grains have been elongated in the direction of machining. It also appears that in all such experimental conditions where cutting fluid was not applied (experiment numbers 5 to 9) and cutting speed, depth-of-cut and feed are relatively higher, the strain is less and grain size is large. It may be inferred that dynamic strain relieving occurred in all such cases. Further, when the cutting fluid was used at a relatively higher speed the temperature rose quickly, but due to the cooling effect of the cutting fluid the temperature decreased rapidly. This might have caused lower strains and formation of small-sized elongated grains. Such conditions are observed in experimental conditions 16 to 18.

7.36%), which shows that optimal combination of the slab milling process parameters is good enough to meet the requirement.

5 Conclusion This paper has presented an effective approach for the optimization of the slab milling process of HSLA steel with multi-performance characteristics based on the combined Taguchi method and Grey relational analysis. Based on the results of the present study, the following conclusions are drawn: &

&

4.3 Confirmation test After obtaining the optimal level of the slab milling process parameters, the next step is to verify the percentage change of Grey relational grade between predicted and experimental values for the optimal combination. Table 16 compares the results of the confirmation experiments using the optimal slab milling process parameters (A2B1C1D3) obtained by the proposed method. As shown in Table 10, that Grey relational grade improved from 0.7100 to 0.7623 (an improvement of

& &

Table 16 Results of confirmation test Optimal machining parameters

Level Surface roughness Shear strain Micro-hardness Grey relational grade

Prediction

Experiment

A2B1C1D3

A2B1C1D3 1.0000 0.4640 0.8228 0.7623

0.7100

&

% improvement

&

7.36

The optimum combination of slab milling parameters and their levels for the optimum multi-performance characteristics of the slab milling process are A2B1C1D3 (i.e. cutting fluid—present, cutting speed—1,800 r.p.m., feed—150 mm/min and depth-of-cut—0.23 mm). All four machining parameters, i.e. cutting fluid, cutting speed, feed and depth-of-cut, significantly affect the multi-performance characteristics of the slab milling process investigated in this study. The percent contributions of depth-of-cut, feed, cutting speed and cutting fluid are 33.76, 24.01, 16.29 and 13.21, respectively. An improvement of 7.36% in the multi-performance characteristics, i.e. Grey relational grade, was achieved through this approach. The grains get elongated in the direction of machining. The grain growth is restricted owing to the use of cutting fluid as it reduces the after effects of heat produced during machining. The absence of cutting fluid during machining leads to grain growth due to prevailing higher temperature during machining, which indeed alters the surface microstructure. The interactions between cutting fluid and feed (A×C), cutting speed and feed (B×C), cutting speed and depthof-cut (B×D) and feed and depth-of-cut (C×D) significantly affect the multi-performance characteristic, i.e. Grey relational grade.


871

References 10. 1. Field M, Kahles J (1971) Review of surface integrity of machined components. Ann CIRP 20(2):153–163 2. Sun J, Guo YB (2009) A comprehensive experimental study on surface integrity by end milling Ti–6Al–4V. J Mater Process Technol 41(12):1705–1719 3. El-Gallab M, Sklad M (1998) Machining of Al/SiC particulate metal matrix composites: part II: workpiece surface integrity. J Mater Process Technol 209(8):4036–4042 4. El-Khabeery MM, El-Axir MH (2001) Experimental techniques for studying the effects of milling roller-burnishing parameters on surface integrity. Int J Mach Tool Manuf 41(12):1705–1719 5. Che-Haron CH (2001) Tool life and surface integrity in turning titanium alloy. J Mater Process Technol 118(1–3):231–237 6. Axinte DA, Dewes RC (2002) Surface integrity of hot work tool steel after high speed milling—experimental data and empirical models. J Mater Process Technol 127(3):325–335 7. Dhar NR, Paul S, Chattopadhayay AB (2002) Machining of AISI 4140 steel under cryogenic cooling—tool wear, surface roughness and dimensional deviation. J Mater Proc Technol 123(3):483–489 8. Novovic D, Dewes RC, Aspinwall DK, Voice W, Bowen P (2004) The effect of machined topography and surface integrity on fatigue life. Int J Mach Tool Manuf 44(2–3):125–134 9. Dhar NR, Kamruzzaman M, Mahiuddin A (2006) Effect of minimum quantity lubrication (MQL) on tool wear and surface

11.

12. 13.

14.

15.

16. 17.

roughness in turning AISI-4340 steel. J Mater Process Technol 172(2):299–304 Basavarajappa S, Chandramohan G, Prabu M, Mukund K, Ashwin M (2007) Drilling of hybrid metal matrix composites— workpiece surface integrity. Int J Mach Tool Manuf 47(1):92–96 Javidi A, Rieger U, Eichlseder W (2008) The effect of machining on the surface integrity and fatigue life. Intern J Fatigue 30 (10–11):2050–2055 Ross PJ (1988) Taguchi techniques for quality engineering. McGraw-Hill, New York Siddiquee AN, Khan ZA, Mallick Z (2010) Grey relational analysis coupled with principal component analysis for optimization design of the process parameters in in-feed centreless cylindrical. Int J Adv Manuf Technol 46:983–992 Khan ZA, Kamaruddin S, Siddiquee AN (2010) Feasibility study of use of recycled high density polyethylene and multi response optimization of injection moulding parameters using combined Grey relational and principal component analyses. Mater Des 31:2925–2931 Yang YY, Shie JR, Huang CH (2006) Optimization of dry machining parameters for high purity graphite in end-milling process. Mater Manuf Process 21:832–837 Phadke SM (1989) Quality engineering using robust design. Prentice-Hall, New York Unal R, Stanley DO, Joyner CR (1993) Propulsion system design optimization using the Taguchi method. IEEE Trans Eng Manag 40:315–322