Effect of Machining Parameters on Surface Roughness and Metal Removal Rate in Micro/ Meso Scale Machine (Micromilling)

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ISSN 2395-1621 Effect of Machining Parameters on

Surface Roughness and Metal Removal Rate in Micro/ Meso Scale Machine

(Micromilling)

#1T.S.Jagtap, ^#2N. D. Misal

#12Mechanical Engineering Department

SVERI’s College of Engineering, Pandharpur, Maharashtra, India

ABSTRACT ARTICLE INFO

This study was carried out to understand the micro milling of Aluminium Alloy A356 material with end mill and consist of experimental work and analysis of MRR and surface roughness. In experimental work, micro-milling experiments were carried out using Taguchi method. The effect of spindle speed, feed rate and depth of cut on MRR and surface roughness were studied. The effect of control parameters on responses was determined by analysis of variance by using MINITAB 15 analyzing software.

Keywords: Spindle Speed, Feed Rate, Depth of cut, Surface roughness, Metal Removal Rate, ANOVA

Article History Received: 5^thJuly 2017 Received in revised form : 5^th July 2017

Accepted: 7^th July 2017 Published online : 8^th July 2017

I. INTRODUCTION

The worldwide trend for miniaturization continues to grow for micro manufacturing system such as biomedical, automotive, defence, electronics and aerospace. Non- traditional methods such as electro discharge machining (EDM), electrochemical machining (ECM), laser machining, focused ion beam machining are used to produce micro components, but these are limited due to limited work piece material selection, poor conductivity and high cost [1]. Apart from these non-traditional methods, micro machining (micro drilling, micro milling, micro grinding, micro turning) has gained increasing interest in recent years. One of the micro machining processes is micro- milling. It uses miniature cutting tools which have been adopted from macro tools and many works have conducted to design and fabricate micro milling tools [2]. Manufacturing of complex 2D and 3D shapes is possible with micro-milling and Micro- components made from variety of engineering materials such as polymer [3], aluminium [4], steel [5], copper [6], brass [7], glass, composites were machined with micro- milling. In some micro milling studies different design of experiment methods such as response surface, full factorial and Taguchi were used. Micro-milling is a process that utilizes end mills of diameter from 10 to 500 μm. The micro milling process has several salient feature

that differentiate it from the macro-end-milling process [8].

Surasit Rawangwong et al. [9] investigated the effect of main factors of the surface roughness in aluminium 7075- T6 face milling. The study was conducted by using CNC milling machine with fine type carbide tool with twin cutting edge. The controlled factors were the speed, feed rate and the depth of cut. The result with factorial design showed that the feed ratio and the speed affect surface roughness while the depth did not affect the surface roughness.

Lohithaksha M Maiyar et al.[10] investigated the end milling operation for Inconel 718 super alloy using grey relational analysis. Nine experimental runs based on an L9 orthogonal array were performed. Cutting speed, feed rate and depth of cut are optimized with considerations of multiple performance characteristics namely surface roughness and material removal rate. A grey relational grade obtained is used to solve the end milling process with the multiple performance characteristics.

Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor. Finally, confirmation tests were performed to make a comparison between the experimental results and the model developed.

In current study spindle speed, feed rate and depth of cut were chosen as machining factors in order to

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(MRR) and SURFACE ROUGHNESS. So the important point of measurement used in this study was to investigate a relationship between the micro-milling parameters and machinability, performance, including MRR and SURFACE ROUGHNESS. This study presented the optimization of micro-milling for Aluminium Alloy A356 so as to minimize surface roughness simultaneously using Taguchi based L9 array

II. DESIGNOFEXPERIMENTS

According to Taguchi method of experimentation the procedure of Experimentation is generalized in following manner; 1] Selection of material 2] Selecting Input parameters 3] Construction of Taguchi L9 array 4] tool selection 5] Experimentation.

A. Material Selection

The micro- milling tests were conducted on the micro machining centre. In the micro milling experiments, Aluminium Alloy A356 material with Brinell hardness of 70 HB was used as work piece material, which has a dimension of 25mm x30mm blocks. The static run out of tool shaft was measured via dial gauge before each experiment and the values were found to be smaller than 5 μm. The material used has shown below in the figure.

Figure 1: aluminium A356 material B. Selecting Input parameters

Input controlled variables such as Spindle speed (rpm), feed rate (mm/min) and Depth of cut (mm) are designed according to the highest permissible values that the material can withstand with and according machine specifications so as to achieve optimum values of MRR and SURFACE ROUGHNESS. So here the different set of values for different levels is shown below in Table no.

1

Table 1: Machining Parameters with different levels INPUT

VARIABLES

Units LEVELS

Level 1 Level 2 Level 3 Spindle Speed rpm 1000 1500 2000

Feed Rate mm/min 50 100 150

Depth of Cut mm 0.05 0.07 0.09

C. Construction of Taguchi L9 array

Taguchi method uses specially constructed tables named as “orthogonal array” to design the experiments and using

of these orthogonal arrays tests the number of experiments. As a result, experimental cost, effort and time will reduce. Taguchi’s L9 orthogonal array was used for the experimental design in order to achieve the aims of how the controlled factors affect the output factors and what the optimal micro-milling controlled parameters to obtain lower tool wear, MRR and Surface Roughness.

Spindle speed, feed rate and depth of cut were considered as controlled factors and MRR and Surface Roughness were selected as output factors.

Table 2: Taguchi (L9orthogonal array) Test No Spindle speed Feed Rate Depth of cut

1 1000 50 0.05

2 1000 100 0.07

3 1000 150 0.09

4 1500 50 0.07

5 1500 100 0.09

6 1500 150 0.07

7 2000 50 0.09

8 2000 100 0.05

9 2000 150 0.07

D. Tool Material Selection

Table 3: Tool material Specifications Sr.

No.

Particulars Specifications Remarks 1 Flat end

milling cutter

1] 2 mm diameter 2] 45 mm end mill

length 3] 3mm shank

diameter

With dry/wet conditions

III. EXPERIMENTATION

Experiment was carried out on the micro machining Centre and all the work is done according to construction of Taguchi L9 ORTHOGONAL ARRAY in pre deterministic way. Here the figure 2 below gives the idea about a micro machining and through all of experiments machining time and width of cut was kept constants.

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The whole experiments were carried out at R &

D department (SVERI’s COE Pandharpur). Again for experimental analysis the table 4 shown below conceptualize the experimentation and final result matrix:

Table 4: Final Design Matrix and Results Sr.

No.

Spindle speed (RPM)

Feed rate (mm/m

in)

Depth of Cut (mm)

MRR (g/min)

Ra (μm)

1 1000 50 0.05 0.00813 0.8125

2 1000 100 0.07 0.01370 0.8653

3 1000 150 0.09 0.02551 0.9123

4 1500 50 0.07 0.00712 0.7850

5 1500 100 0.09 0.01425 0.8560

6 1500 150 0.05 0.00714 0.8125

7 2000 50 0.09 0.00923 0.7852

8 2000 100 0.05 0.00721 0.7540

9 2000 150 0.07 0.00812 0.8251

3.1 Evaluation of MRR

The material MRR is expressed as the ratio of the difference of weight of the work-piece before and after machining measured by precision weight balance to the machining time

W_B-W_A MRR =---/T Where as

W_B = Weight of work-piece before machining W_A = Weight of work-piece after machining.

T = Machining time (constant T = 2 min) IV. RESULTANDDISCUSSION

4.1 Analysis of Metal Removing Rate

4.1.1 Modal Analysis of Metal Removing Rate Table 5: Regression coefficients for MRR

Predictor Coef SE Coef T P

Constant 0.001653 0.006187 0.27 0.800

Spindle - 0.00000243 - 0.023

speed 0.00000759 3.12

Feed rate 0.00005430 0.00002431 2.23 0.076 Depth of cut 0.22092 0.06076 3.64 0.015 The regression equation is

MRR (g/Min) = 0.00165 - 0.000008 Spindle speed + 0.000054 Feed rate+0.221 Depth of cut

Table 6: Analysis of Variance for MRR Control

Factor

DO F

Seq. SS Adj MS F- ratio P- value Regression 3 0.00024

7845

0.00008 2615

9.32 0.017 Residual

Error

5 0.00004 4309

0.00000 8862

0.000 Spindle speed 1 0.00008

6488 Feed rate 1 0.00004

4227 Depth of cut 1 0.00011

7130

Total 8 0.00029

2154

S = 0.00297686 R-Sq = 84.8% R-Sq(adj)

=75.7%

4.1.2 Residual Plot for MRR

Figure 3 Main Effect plot for MRR

Figure 4 Interaction Plot for MRR 4.2

4.3 Analysis of Surface Roughness

42.1 Modal Analysis of Surface Roughness

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Predictor Coef SE Coef T P

Constant 0.77848 0.02035 38.25 0.000 Spindle

speed

- 0.00007527

0.00000800 -9.41 0.000 Feed rate 0.00055733 0.00007996 6.97 0.001 Depth of cut 1.4542 0.1999 7.27 0.001

Ra (µm) = 0.778 - 0.000075 Spindle speed + 0.000557 Feed rate + 1.45 Depth of cut

Table 8 Analysis of Variance for R_a

Source DF Seq SS Adj MS F P

Regression 3 0.0182320 0.0060773 63.37 0.000 Residual

Error

5 0.0004795 0.0000959 Spindle

Speed

1 0.0084976 Feed rate 1 0.000044227 Depth of

cut

1 0.0046593

Total 8 0.0187115

S=0.00979270 R-Sq = 97.4% R-Sq(adj) = 95.9%

4.2.2 Residual plots for surface Roughness

Figure 5 Main Effect plot for Surface roughness

Figure 6 Interaction plot for Surface roughness

V. CONCLUSION

In this study, the effects of spindle speed, feed rate and depth of cut on MRR and surface roughness during micro-milling of mild steel were investigated using Taguchi experimental design method. All data gathered in the experimental studies were used to formulate and analyzed using MINITAB 15 software. Responses were used alone in optimization study as an objective function.

From multi-objective optimization it was concluded that the optimal values for minimizing tool wear were spindle speed of 1000 rpm, feed rate of 150 mm/min and depth of cut of 0.09mm. With these values maximum metal removing rate (MRR) and minimum Surface Roughness can be achieved. Tool wear increased with spindle speed.

Multi objective optimization is carried out by using analysis of variance ANOVA to optimize two responses simultaneously (to maximize MRR and minimize surface roughness) in one set of input variables. It is observed that, maximum MRR and minimum surface roughness was obtained simultaneously when the work piece was employed to the spindle speed of 1000 rpm, feed rate of 150 mm/min and depth of cut of 0.09 mm. It is also observed that, to obtain maximum MRR (0.02551) and minimum surface roughness simultaneously when employ low range of axial depth of cut and optimum range of spindle speed with moderate feed rate.

ACKNOWLEDGEMENT

I am thankful to SVERI’s College of Engineering Pandharpur. I also indebt thankful to Prof. R. B.

Kapurkar, Prof, S W Wangikar of SVERI’s,‟College of engineering Pandharpur.

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