EXPERIMENTAL INVESTIGATIONS OF OPTIMAL 2D SURFACE TEXTURE PARAMETERS USING GRA WHEN MACHINING UNDER

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 173–182 © TJPRC Pvt. Ltd.

EXPERIMENTAL INVESTIGATIONS OF OPTIMAL 2D SURFACE TEXTURE PARAMETERS USING GRA WHEN MACHINING UNDER DRY AND AEROSOLMIST CONDITIONS IN TURN-MILL OPERATION A. M. VENKATA PRAVEEN1, K. ARUN VIKRAM2 & K. SURESH3 1 2,3

Research Scholar, Department of Mechanical Engineering, GITAM (Deemed to be University), Andhra Pradesh, India

Assistant Professor, Department of Mechanical Engineering, GITAM (Deemed to be University), Andhra Pradesh, India

ABSTRACT Improvement in surface texture which replicates the surface finish of a machined product has demanded improved modern technology for proper functioning, aesthetics and longer life of the product. This research studies on experimental 2-dimensional surface roughness textures (like average surface roughness-Ra, root mean square deviation of profile-Rq and maximum height of profile-RZ) as responses for optimal determination of machining parameters using Grey Relational Analysis (GRA). Study of machined 2D surface texture parameters is carried on AISI 304 stainless steel material under dry and aerosol-mist conditions. Multi-axis with C-axis rotational operation such as orthogonal turn-mill feed rate and depth of cut are taken as machining parameters, while Taguchi L16 design of experiments is considered for experimentation with Analysis of variance for predicting significance of machining parameters. In this paper, the novelty of machining and analyzing the machined surface texture parameters over AISI 304 in orthogonal turn-mill operation on C-axis machining center where both workpiece and tool rotates simultaneously is done. The application for attempting multi-surface texture parameters optimization using GRA is also shown.

KEYWORDS: ANOVA, C-Axis, GRA, Roughness, Surface Texture & Turn-Mill

Original Article

machining operation is carried on a CNC Turn-mill machine with a constant C-axis rotation. Tool spindle speed, tool

Received: Mar 12, 2020; Accepted: Apr 02, 2020; Published: Apr 29, 2020; Paper Id.: IJMPERDJUN202016

1. INTRODUCTION The fluctuations between the tool and workpiece in metal machining generate varying amplitudes of asperities and surface valleys with varying spacing. Surface texture which occurs due to material removal processes, indicate the surface finish patterns produced by deviations of asperities and valleys. Surface texture profiles which indicates the surface roughness which are generated after machining of material comprise the error of form, waviness, lay, flows and roughness, Researcher like Prasanta Sahoo et al, (2012) focused study on fractal dimension generations in various machining processes on AISI 1040 mild steel. They have tabulated surface roughness in end milling, turning, grinding and Electron Discharge machining and used artificial neural network for predicting fractal dimension models.

Surface texture which is the characteristics of machined material surface is the random and repetitive deviations from the nominal surface showing waviness, roughness and lays. These characteristics are important requirements for proper geometrical drawing and tolerance of machined parts assembly and to control friction between surfaces. Surface irregularities with larger wavelength are known to be called as “waviness” is a result of deflections, vibrations etc between machine-tool-workpiece, while Lay represents the principal direction of surface pattern which is predominant due to the tool-workpiece contact motion mechanism. On the other hand, surface www.tjprc.org

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roughness which is known to be the primary texture is the result of inherent actions between cutting tool and work material machining mechanism thereby causing irregularities on the machined material surface. It can be recorded and analyzed using non-contact 3-dimensional (3D) profile meters (like couple charged device-CCD) or 2dimensional (2D)-contact profile meters (like roughness testers). Over decades engineers and researchers have been using 2D measuring devices for measuring the roughness of a surface with various parameters among which, the most common used are arithmetic mean roughness (Ra). Ramaswamy, (2001) compared and presented 2D and 3D roughness parameters and concludes the 3D parameters are of more much use for dynamic analysis like spatial, and volume parameters. Similarly, Tao Wang et al.

(2015) presented a study on surface roughness generated and evaluation on aluminium alloy while machining with high speed milling using typical 2D (Ra and Rz) and 3D (Sa and Sq) surface roughness parameters and they indicated 3D parameters to be more capable.

The 3D profiles representing Skew and kurtosis parameters are very useful and easy made for machine learning but has a deficiency in high cost investment and skills requirement. On the other hand, surface roughness in 2D representation using roughness parameters (like average surface roughness-Ra, root mean square deviation of profile-Rq and maximum height of profile-RZ) is less costly and minimal skill requirement is sufficient and so, in this work 2D roughness parameters are attempted for study and optimization. The roughness parameter ‘Rq’ which represents the standard deviation of the height distribution over the taken sampling length provides the same information as Ra, on the other side, ‘Rz’ corresponds to frequent checking of the profile to know its inclusion of protruding peaks that might affect static or sliding contact function. Leandro Tonietto et al, (2019) performed quality evaluation of a material’s surface through analysis of roughness. They proposed a new method to evaluate surface roughness by combining qualitative and quantitative assessment of surface roughness. They reported the relations between 2D and 3D roughness parameters.

Manufacturing mostly involves in indentifying the optimal combination of machining parameters to increase surface finish for greater aesthetics, life and contact between parts, so in the present work numerical methods like Grey Relational Analysis (GRA) which normalize and gives weightage to each response for the combination of qualitative and quantitative criteria is adopted for multi-response optimality determination. Taguchi’s concept which is frequently used as a method for design of experiments lags in carrying multi-response optimization and so method such as Grey Relational Analysis (GRA) can be integrated (Khalilpourazary et al, 2014; Lal S et al, 2013; Ahilan, C et al. 2010). Experimental investigation of surface finish/roughness 2D parameters like average surface roughness (Ra- µm), root mean square deviation of profile (Rq-µm) and maximum height of profile (RZ- µm) are considered for study while machining AISI 304 stainless steel cylindrical workpiece for multi-response optimal studies using GRA. Taguchi Design of experiments with orthogonal array concept with L16 combination of experiments is taken as experimental design while plain turning under dry and aerosol-mist conditions with coated carbide end mill cutters and constant workpiece rotation as C-axis.

2. MATERIAL AND EQUIPMENT UTILIZED Stainless steels have corrosion resistance and contain minimum of 0.10% chromium. Stainless steels with austenitic structures have hardness not a guide for speeds. American Iron and Steel Institute- AISI 304 has high domestic applications and the chemical composition of AISI 304 includes C 0.10%, Mn 1.5%, Ni 9.5%, Cr 19.0% and remaining Fe with 36 mm diameter and 80 mm long are used. MITUTOYO surface roughness tester (model: SJ 301) was used to measure surface roughness parameters. Physical Vapour Deposition (PVD) TiAlN coated carbide solid end mill cutters of Kennametal make with 4CH1000ML032A KC633M grade are used for orthogonal turn-mill operation. C-axis Turn-mill machine of Impact Factor (JCC): 8.8746

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PMK make with model-TMC-XL-200 with Fanuc 0i control system, shown in figure 1 is utilized for experimentation.

Figure 1: CNC Turn-Mill Center and Aerosol-Mmst Setup.

Machining was carried under dry and aerosol-mist water soluble oil (1:20 with 1litre of oil and 20 liters of water) cutting fluid with an air pressure of 5.5 bar and 30 ml/min condition. Compressor with 24 liter capacity was used for aerosol-mist environment and connected to a nozzle having an air and cutting fluid mixture vortex tube.

3. EXPERIMENTAL DESIGN AND METHODOLOGY Experimental design of 16 experiments (L16) using Taguchi method with three machining parameters having four levels each, as shown in Table 1 is used for machining in orthogonal turn-mill operation on AISI 304 stainless steel workpiece.

Each new combination of experiment was done using a new coated carbide four flute end mill cutter tips.

Table 1: Machining Parameters and their Levels Machining Parameter Level 1 Level 2 Level 3 Level 4 A: Tool speed (rpm) 1300 1500 1700 1900 B: Tool Feed (mm/min) 0.10 0.14 0.14 0.22 C: Depth of cut (mm) 0.3 0.6 0.9 1.2

Table 2a: Experiment Design and Measured Responses in dry Orthogonal Turn-Mill Machining Exp Tool Speed Tool Feed Depth of cut Ra Rq No (rpm) (mm/min) (mm) (µm) (µm) 1 1300 0.10 0.3 1.28 1.56 2 1300 0.14 0.6 1.41 1.75 3 1300 0.18 0.9 1.60 1.91 4 1300 0.22 1.2 1.71 2.07 5 1500 0.10 0.6 1.16 1.45 6 1500 0.14 0.3 1.29 1.63 7 1500 0.18 1.2 1.38 1.51 8 1500 0.22 0.9 1.54 1.94 9 1700 0.10 0.9 1.27 1.55 10 1700 0.14 1.2 1.20 1.58 11 1700 0.18 0.3 1.34 1.70 12 1700 0.22 0.6 1.44 1.71 13 1900 0.10 1.2 1.10 1.35 14 1900 0.14 0.9 1.18 1.46 15 1900 0.18 0.6 1.27 1.55 16 1900 0.22 0.3 1.49 1.86 Average surface roughness (Ra); Root mean square deviation of profile (Rq); Maximum height of profile (RZ)

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Rz (µm) 7.46 8.24 8.54 8.64 6.68 8.45 5.10 9.70 7.19 7.87 8.46 7.54 6.44 7.29 7.19 8.83

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Surface texture parameters are taken as responses (like average surface roughness (Ra- µm), root mean square deviation of profile (Rq-µm) and maximum height of profile (RZ- µm) are measured using surface roughness tester and are given in Table 2a and Table 2b for dry and aerosol-mist condition machining respectively. The surface R-profile with Gauss filtered, ISO 1997 standard and cut of length of 0.8 mm are shown in figure 2a and figure 2b for dry and aerosolmist conditions respectively.

Figure 2a: Surface Roughness Profile of Experiment-1 under Dry Machining.

Responses thus obtained are used for machining parameter significances using Analysis of Variance (ANOVA).

In addition, they are also used to evaluate multi-response optimality using Gray relational analysis. In the last, a sensitive analysis of GRA concept is carried by varying the weightage of the responses.

Exp No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Table 2b: Experiment Design and Responses in Aerosol-Mist Machining Tool Speed Tool feed Depth of cut Ra Rq (rpm) (mm/min) (mm) (µm) (µm) 1300 0.10 0.3 1.20 1.55 1300 0.14 0.6 1.40 1.72 1300 0.18 0.9 1.56 1.92 1300 0.22 1.2 1.68 2.10 1500 0.10 0.6 0.99 1.25 1500 0.14 0.3 1.11 1.31 1500 0.18 1.2 1.14 1.41 1500 0.22 0.9 1.29 1.49 1700 0.10 0.9 0.92 1.12 1700 0.14 1.2 0.97 1.18 1700 0.18 0.3 1.22 1.49 1700 0.22 0.6 1.40 1.72 1900 0.10 1.2 0.73 0.88 1900 0.14 0.9 1.02 1.25 1900 0.18 0.6 1.10 1.32 1900 0.22 0.3 1.48 1.86

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Rz (µm) 7.87 8.14 9.76 10.10 6.59 5.35 7.37 6.40 5.11 5.39 7.62 8.14 4.22 5.55 5.73 9.71

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Figure 2b: Surface Roughness Profile of Experiment-1 under Aerosol-Mist Machining.

3.1. Analysis of Variance Statistical models are primarily based on hypothesis of accepting or rejecting, such as in case of Analysis of Variance (ANOVA). ANOVA uses analysis of the variance between groups and in-between groups and accepts or reject the hypothesis based on statistical value like Fisher’s value (F-value). It treats the hypothesis confirmation or rejection based on comparing the F-value with the critical values. The tabulation and equations used in ANOVA are shown in Table 3.

ANOVA with significance level of 95% is carried in both conditions of machining and is given in Table 4a and Table 4b.

Source Among or Between “Groups” Within groups or “Error” Total

Table 3: ANOVA format for Tabulation and Formulae Sum of Squares (SS) Mean Square (MS) DF DFG = (n-1) DFE = (DFT-DFGtotal) DFT = (N-1)

SSBetween

MSG = (SSgroup)/DFgroup)

SSwithin

MSE = (SSError)/DFError)

Feval

Remarks

(MSG/MSE)

Significant or not

SSTotal (1) (2) (3)

Source A B C Error A B C Error A B www.tjprc.org

Table 4a: ANOVA of Generated Responses in Dry Machining DF SS MS F Remarks Surface roughness (Ra) (F-Critical of 3,6 is 4.757) 3 0.128 0.043 12.5 Significant (S) 3 0.274 0.091 26.4 Highly significant (HS) 3 0.012 0.004 1.15 Not significant (NS) 6 0.0207 0.003 Root mean deviation (Rq) 3 0.155 0.052 5.7 NS 3 0.366 0.122 13.5 HS 3 0.027 0.009 0.96 NS 6 0.056 0.009 Maximum profile height (Rz) 3 1.551 0.521 0.68 NS 3 6.952 2.317 3.04 NS SCOPUS Indexed Journal

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C Error

Source A B C Error A B C Error A B C Error

3 6

4.572 5.408

1.52 0.762

1.99

NS

Table 4b: ANOVA of Generated Responses in Aerosol-Mist Machining DF SS MS F Surface roughness (Ra) (F-Critical of 3,6 is 4.757) 3 0.364 0.121 20.1 3 0.541 0.180 30.2 3 0.033 0.011 1.83 6 0.04 0.006 Root mean deviation (Rq) 3 0.656 0.218 12.8 3 0.768 0.256 15.1 3 0.058 0.019 1.11 6 0.104 0.017 Maximum profile height (Rz) 3 19.43 6.48 4.59 3 19.17 6.39 4.53 3 2.21 0.74 0.52 6 8.43 1.41

Remarks HS HS NS

S HS NS

NS NS NS

3.2. Grey Relational Analysis Taguchi’s concept stands as a base for optimization in manufacturing but need to coupled with another analytical method for multi-response optimization, as Taguchi concept suffers in doing it (Arun et al. (2018)). So, in this research, method like Grey Relational Analysis (GRA) is being coupled for the response to multi-optimize the responses.

GRA developed by Deng, adopt normalizing the responses for compatibility of comparison using lower-the-better and higher-the-better concept of signal-to-noise ratio, as given in equation-4a and 4b.

For Lower-is-Better (LB):

(4a)

For Higher-is-Better (HB):

(4b)

where Xi(j) is value of response of ith experiment, max(xi(j)) and min(xi(j)) are the smallest and largest values of Xi(j) respectively.

Note: In this work, the surface roughness parameters need to have low noise, thereby leading to use lower-isbetter equation. A sample calculation of experiment-1 of dry condition is shown: For Lower-is-Better (LB):

= [(1.71-1.28/(1.71-1.10)] = 0.7049

Later, the deviation in sequence Δoi(k) of each response is determined using equation-5 (5) Sample calculation: the deviation sequence

= |0.7049-1|=0.2951

Later, the deviation which are determined is used for calculating Grey relational coefficient correlation between responses by giving equal weight age (distinguishable coefficient) as given in equation-6.

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(6) where Δmin is smallest value of the normalized values, Δmax is maximum value of the normalized values, ζ is distinguishing coefficient and ranges from 0 to 1 and had been assumed as 0.5 (Khalilpourazary et al, 2014).

Sample calculation of Grey relational coefficient: γi = ((0+0.5*1)/((0.2951+(0.5*1))) = 0.629 Lastly, the grey relational coefficients of all the responses in an experiment is averaged and graded for ranking using Eq-7.

(7) where “nr” is total number of responses.

Sample calculation of Grey relational grade: = (0.629+0.632+0.494)/3 = 0.585 The grade which gets highest rank is taken as optimal combination for multi-response optimization and is tabulated in Table 5a and Table 5b for dry and aerosol-mist conditions respectively.

Table 5a: Normalization, Grey Relational Coefficient and Grade for Ranking in Dry Machining Normalization based on LB/HB Grey Relational Coefficients Grey Relational Exp. No. Rank Ra Rq Rz Rq Rz Grade Ra (µm) (µm) (µm) (µm) (µm) (µm) 1 0.7049 0.7083 0.4870 0.629 0.632 0.494 0.585 2 0.4918 0.4444 0.3174 0.496 0.474 0.423 0.464 3 0.1803 0.2222 0.2522 0.379 0.391 0.401 0.390 4 0.0000 0.0000 0.2304 0.333 0.333 0.394 0.354 5 0.9016 0.8611 0.6565 0.836 0.783 0.593 0.737 6 0.6885 0.6111 0.2717 0.616 0.563 0.407 0.529 7 0.5410 0.7778 1.0000 0.521 0.692 1.000 0.738 8 0.2787 0.1806 0.0000 0.409 0.379 0.333 0.374 9 0.7213 0.7222 0.5457 0.642 0.643 0.524 0.603 10 0.8361 0.6806 0.3978 0.753 0.610 0.454 0.606 11 0.6066 0.5139 0.2696 0.560 0.507 0.406 0.491 12 0.4426 0.5000 0.4696 0.473 0.500 0.485 0.486 13 1.0000 1.0000 0.7087 1.000 1.000 0.632 0.877 1 14 0.8689 0.8472 0.5239 0.792 0.766 0.512 0.690 15 0.7213 0.7222 0.5457 0.642 0.643 0.524 0.603 16 0.3607 0.2917 0.1891 0.439 0.414 0.381 0.411

Table 5b: Normalization, Grey Relational Coefficient and Grade for Ranking in Aerosol-Mist Machining Normalization based on LB/HB Grey Relational Coefficients Grey Relational Exp. No. Rank Ra Rq Rz Rq Rz Grade Ra (µm) (µm) (µm) (µm) (µm) (µm) 1 0.5053 0.4508 0.3793 0.503 0.477 0.446 0.475 2 0.2947 0.3115 0.3333 0.415 0.421 0.429 0.421 3 0.1263 0.1475 0.0578 0.364 0.370 0.347 0.360 4 0.0000 0.0000 0.0000 0.333 0.333 0.333 0.333 5 0.7263 0.6967 0.5969 0.646 0.622 0.554 0.607 www.tjprc.org

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6 7 8 9 10 11 12 13 14 15 16

0.6000 0.5684 0.4105 0.8000 0.7474 0.4842 0.2947 1.0000 0.6947 0.6105 0.2105

0.6475 0.5656 0.5000 0.8033 0.7541 0.5000 0.3115 1.0000 0.6967 0.6393 0.1967

0.8078 0.4643 0.6293 0.8486 0.8010 0.4218 0.3333 1.0000 0.7738 0.7432 0.0663

0.556 0.537 0.459 0.714 0.664 0.492 0.415 1.000 0.621 0.562 0.388

0.587 0.535 0.500 0.718 0.670 0.500 0.421 1.000 0.622 0.581 0.384

0.722 0.483 0.574 0.768 0.715 0.464 0.429 1.000 0.689 0.661 0.349

0.621 0.518 0.511 0.733 0.683 0.485 0.421 1.000 0.644 0.601 0.373

1

4. RESULTS AND DISCUSSIONS Basing on ANOVA for finding significance of machining parameters, the results predict tool feed to be more predominant in generating surface roughness and later followed by tool speed. The depth of cut for material removal is having less

Maximum height (micron)

impact in generating roughness.

Roughness (micron)

1.80 1.60 1.40 1.20 1.00 0.80 0.60

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 1

2

3

Experiment number Roughness-Ra in dry condition

4

5

6

7

8

9

10 11 12 13 14 15 16

Experiment number

Roughness -Ra in mist condition

Figure 3a: Comparison Plots of Roughness Values in dry and Mist Conditions.

Maximum height-Rz in mist condition

Maximum height-Rz in dry condition

Figure 3b: Comparison Plots of Maximum Valley Heights in dry and Mist Conditions.

Observing graph plot 3a, it depicts that the aerosol-mist condition machining is much more efficient in generating a better surface finish due to the presence of the cutting fluid acting as lubricant, but graph plot 3b determines that the roughness texture maximum peak heights do not depend on the cutting conditions.

Observing Table 5a and Table 5b, which details the GRA optimal determination in dry and aerosol-mist machining gives the optimal combination as high tool speed in combination with low tool feed irrespective of depth of cut. i.e. using GRA, the optimality obtained is tool speed: 1900 rpm, tool feed: 0.10 mm/min and depth of cut: 1.2 mm which generates surface roughness parameters (Ra = 1.10 µm, Rq = 1.35 µm and Rz = 6.44 µm) in dry machining condition and surface roughness parameters (Ra = 0.73 µm, Rq = 0.88 µm and Rz = 4.22 µm) in dry machining condition.

5. CONCLUSIONS 

The chips generated in machining are found to be of small helical powder in nature, due to relative motion between tool and workpiece. Moreover, the mechanism of machining is intermittent in nature breaking the chips into small portions.



The powder like chip generated are not easy to be disposed while machining in aerosol-mist condition rather than dry condition, due to fact that the chips may be blown away and be mixed with cutting fluid spray.



The multi-objective optimal combination showed using GRA defines that, the cutting fluid application in metal

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machining do increase surface finish but not the optimal machining combination. GRA depends on simple and vague weightage of responses under consideration, hence sensitive analysis of due weightage is also carried in this research work and reflected the same results for every change in weightage. 

Analyzing ANOVA of the machining parameters on the generated surface texture responses, the tool feed and tool speed together reflects their belonging in generating surface roughness in dry and aerosol-mist machining conditions. On the other hand, irrespective of condition of machining the peak deviation in surface texture (Rz) cannot be predicted because the occurrence of maximum deviation can be due to other factors like misalignment, forces, vibrations, eccentricity etc.

ACKNOWLEDGEMENT The corresponding author would like to thank Science and Engineering Research Board (SERB) under Department of Science and Technology (DST) with File no: EEQ/2016/000395 for providing financial support for procuring tools to carry out the experiments.

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