Optimization of Cutting Process Parameters on AL6061 Using ANOVA and TAGUCHI Method

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ScienceDirect Materials Today: Proceedings 4 (2017) 10845–10849

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AMMMT 2016

Optimization of Cutting Process Parameters on AL6061 Using ANOVA and TAGUCHI Method Niranjan D B1*, G.S.Shivashankar2 Sreenivas Rao K V3, Praveen R4 . 4

123 Department of Mechanical Engineering,SIT Tumkur-572103,Karnataka,India Department of Mechanical Engineering PESITM, Shimoga-577204, Karnataka,India

Abstract Aluminium is the most abundant metal in the Earth’s crust and it is also the second most widely used metal in the world. It has a very good machinability index and there is a need to increase the production while reducing the cost. In this research work, an attempt is made to optimize the cutting parameters such as cutting speed, feed rate and depth of cut in the turning operation of Aluminium Alloy 6061 T6 cylindrical rods using Taguchi method and Analysis of Variance (ANOVA). Better quality of the surface finish is obtained with cutting speed 429 m/min, feed rate 0.05mm/min and depth of cut 1mm. These process parameters are considered as optimum process parameters © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of Advanced Materials, Manufacturing, Management and Thermal Science (AMMMT 2016). Keywords: Surface roughness, Material removal rate, ANOVA, Taguchi method

1. Introduction The latest pattern in assembling is towards expanding the production rate while lessening the production cost. This is accomplished by increasing the material removal rate (MRR). However, the surface finish gets influenced with the expansion in MRR. The impact of cutting parameters on surface completion is examined by IlhanAsilturk et.al. by Taguchi method[1]. Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured goods. More recently this method is being applied to engineering, biotechnology, marketing, advertising etc. Analysis of variance (ANOVA) is a collection of statistical models used in order to analyse the differences between group means and their associated procedures (such as "variation" among and

* Corresponding author. Tel.: 8971876663; E-mail address: [email protected] 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of Advanced Materials, Manufacturing, Management and Thermal Science (AMMMT 2016).

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between groups). Experimental determination of material removal rate is carried out by using CNC machine by Kamal Hassana et. al. [2].P. Jayaraman and L. Mahesh Kumar [3] have applied grey relational analysis in Taguchi method for optimization of machining parameters of aluminium alloy. Machining characteristics have been studied by many researchers for various materials [4-6]. The literature survey indicates that very little work is done on the optimization of cutting parameters on surface finish. Hence this work is aimed at optimizing the cutting parameters such as cutting speed, feed rate and depth of cut in the turning operation of Aluminium Alloy 6061 T6 cylindrical rods using Taguchi method and Analysis of Variance (ANOVA). 2. EXPERIMENTAL DETAILS A CNC Turning Centre was chosen for the experimental work, because it offers a wide range of advantages over the conventional lathe. The work piece used for the experiments was of 40mm diameter solid round rods of Aluminium alloy 6061 T6 as shown in Fig 1(a). The length of the rods was 300mm. The diameter measurements of the rod after every turning operation were made using a Vernier calliper. The length of the rods was measured using a steel ruler. The surface unpleasantness estimations were taken at four distinct focuses specifically A,B,C and D along the breadth of the work piece utilizing a Taylor Hobson Surtronic instrument which is as appeared in Fig 1 (b).

Fig1 (a) Workpiece

Fig1 (b) Taylor Hobson Surtronic instrument

2.1 Process Parameters Input parameters: Three information parameters chosen for the analysis are; Cutting Speed (v), Feed rate (f) and Depth of cut (d). Output parameters: Two output parameters considered are; Surface unpleasantness (Ra) and Material removal rate (MRR). Ra is the math normal of the supreme estimations of the unpleasantness profile ordinates. It is otherwise called Arithmetic Average (AA), Centre Line Average (CLA). The normal harshness is the region between the unpleasantness profile and its mean line, or the vital of the supreme estimation of the harshness profile stature over the assessment length. 2.2 Measurement of Surface Roughness and MRR The experiments were conducted by varying the factors which are listed in Table 1. The observations for MRR and surface roughness were taken using weighing machine and Taylor Hobson Surtronic Instrument respectively. The surface roughness parameter was measured crosswise over four unique focuses (to be specific A, B, C and D) as shown in figure 2 along the breadth of the work piece utilizing Taylor Hobson Surtronic instrument and the

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normal perusing was taken. Just, the Ra estimations of surface unpleasantness were taken and showed in (μm) unit. The material evacuation rates were measured by taking the weights of the work pieces. At first, the work pieces were weighed in the wake of focusing and expelling the flightiness. Then, after each turning operation, the weights were taken. The material removal rates were calculated by taking the difference in weights. The response data are subjected to ANOVA through the analysis of Taguchi Design for determining the significant factors.

Fig 2: Points at which surface roughness readings were taken

Table 1: Factor Table Factor

components

Component 1

Component 2

Component 3

Cutting Speed(m/min)

3

308

369

429

Feed(mm/rev)

3

0.05

0.1

0.15

Depth of Cut(mm)

3

1

1.5

2

3. RESULTS AND DISCUSSION Contour Plots for surface roughness and material removal rate Fig.3.1 – 3.3 demonstrate the form plots of surface harshness for various blends of nourish rate, profundity of cut and cutting velocity. Figures 3.4 – 3.6 demonstrate the form plots of material evacuation rate for various mixes of feed rate, profundity of cut and cutting pace. Contour Plot of Surface Roughnes vs Cutting Speed(m/, Feed Rate(mm/rev 420

Cutting Speed(m/min)

400

380

360

340

320 0.050

Contour Plot of Surface Roughnes vs Cutting Speed(m/, Depth of Cut(mm) Surface Roughness(µm) < 1.0 – 1.5 1.0 – 2.0 1.5 – 2.5 2.0 > 2.5

420

400

Cutting Speed(m/min)

Surface Roughness(µm) < 1.0 – 1.5 1.0 – 2.0 1.5 – 2.5 2.0 > 2.5

380

360

340

320 0.075

0.100

0.125

0.150

Feed Rate(mm/rev)

Fig .3.1: Effect of combination of Feed & Cutting Speed on Surface Roughness

1.0

1.2

1.4

1.6

1.8

2.0

Depth of Cut(mm)

Fig.3.2 : Effect of combination of Depth of Cut & Cutting Speed on Surface Roughness

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Contour Plot of mrr vs Cutting Speed(m/min), feed rate mm/rev

Contour Plot of Surface Roughnes vs Feed Rate(mm/rev, Depth of Cut(mm) Surface Roughness(µm) < 1.0 – 1.5 1.0 – 2.0 1.5 – 2.5 2.0 > 2.5

Feed Rate(mm/rev)

0.125

0.100

mrr < 10 10 – 20 20 – 30 30 – 40 > 40

420

400

Cutting Speed(m/min)

0.150

0.075

380

360

340

320

0.050 1.0

1.2

1.4

1.6

1.8

0.050

2.0

0.075

Fig.3.3: Effect of combination of Depth of Cut & Feed Rate on Surface Roughness

0.125

0.150

Fig. 3.4 :Effect of combination of cutting speed vs feed rate on MRR

Contour Plot of mrr vs feed rate mm/rev, depth of cut (mm)

Contour Plot of mrr vs Speed(N), depth of cut (mm) mrr < 10 10 – 20 20 – 30 30 – 40 > 40

0.125

0.100

0.075

mrr < 10 10 – 20 20 – 30 30 – 40 > 40

420

400

Cutting Speed(m/min)

0.150

feed rate mm/rev

0.100

feed rate mm/rev

Depth of Cut(mm)

380

360

340

320 0.050 1.0

1.2

1.4

1.6

1.8

2.0

depth of cut (mm)

Fig .3.5: Effect of combination of depth of cut vs feed rate on MRR

1.0

1.2

1.4

1.6

1.8

2.0

depth of cut (mm)

Fig.3.6 :Effect of combination of Depth of Cut & Cutting Speed on material removal rate

From the Fig 3.1 it can be seen that base surface harshness happens when the mix of feed rate is under 0.075 mm/rev and cutting rate is under 320 m/min. Great surface completion of under 1μm likewise happens when the feed is under 0.11mm/rev and the cutting rate is more prominent than 400 m/min. From the figure 3.2 it can be seen that base surface harshness happens when the blend of the profundity of cut is under 1.25mm and the cutting rate is under 310 m/min. Great surface completion which is under 1μm likewise happens when the profundity of cut is somewhere around 1.85 and 2 mm and the cutting velocity is above 380 m/min. From the Fig3.4 it can be seen that base surface harshness happens when the mix of the profundity of cut is under 1.15mm and the feed rate is under 0.11 mm/rev. Great surface complete under 1μm additionally happens when the profundity of cut is somewhere around 1.9 and 2 mm and the feed rate is under 0.09 mm/rev. Fig 3.4 demonstrates that when the cutting velocity is more noteworthy than 420 m/min and feed rate is more prominent than 0.150mm/rev, the material expulsion rate of more prominent than 40 cc/min happens. Figure 3.5 demonstrates that for best MRR which is more prominent than 40 cc/min, the feed rate ought to be more than 1.5 mm/rev and the profundity of cut is more than 1.3 mm. Figure 3.6 demonstrate that the best MRR which is more noteworthy than 40 cc/min happens when the cutting pace is more than 420 m/min and the profundity of cut is between 1.3 mm and 1.75 mm.

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4. CONCLUSIONS The following conclusions are drawn from the present work, • • •

The surface unpleasantness diminishes with the expansions in cutting rate, it increments with increment in feed rate and profundity of cut. Better nature of the surface completion is acquired by cutting pace of 429 m/min, nourish rate 0.05mm/min and profundity of cut 1mm. These procedure parameters are considered as ideal procedure parameters. The material expulsion rate is most extreme when the cutting pace is 429m/min, bolster rate is 0.15mm/min and profundity of cut 2mm. These procedure parameters are considered as ideal procedure parameters

References [1] IlhanAsilturk, HarunAkkus- -Measurement, 44 (2011). [2] Kamal Hassana, Anish Kumar, M.P.Garg International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 2,Mar-Apr 2012. [3] P. Jayaraman and L. Mahesh Kumar Procedia Engineering 12/2014. [4] Narayana B. Doddapattar, Chetana S. Batakurki- International Journal of Engineering Research & Technology-Vol.2 Issue 7 July 2013. [5] Md. Tayab Ali, Dr.ThuleswarNath–, IJRMET Vol. 4, Issue 2, May - October 2014. [6] H. R. Ghan, S.D.Ambekar International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 3, Issue 2, March 2014.