O rigin al A rticle

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249-6890; ISSN (E): 2249-8001 Vol. 8, Special Issue 7, Oct 2018, 39-46 © TJPRC Pvt. Ltd.

MULTIRESPONSE OPTIMIZATION OF DRILLING PROCESS PARAMETERS OF AZ 31 MAGNESIUM ALLOYS USING GRAY RELATIONAL ANALYSIS TECHNIQUE A. TAJDEEN1, A. MEGALINGAM2, M. KUMAR3 & B. SELVA KUMAR4 1,3

Assistant Professor, Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam Tamil Nadu, India

2

Associate Professor, Department of Mechanical Engineering Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India 4

Student, Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India

ABSTRACT Nowadays, the use of lighter materials for transportation purposes is still a challenge, especially in the

which have exceptional mechanical properties relative to density as structural materials, allows a remarkable reduction of weight. These alloys have significant challenges in machining. In various types of micromachining, micro-drilling is one of the solid tool based micromachining operation. Generally, micro-drilling is used to generate micro-holes in precision automotive products. In the present work, drilling of AZ 31 magnesium alloy is carried out with, three different Size of HSS tools coated with Carbide, with diameters of 6mm, 7mm and 8 mm, under different spindle speeds and feed rates. Here an endeavor has been made for finding the optimal condition to the micro-drilling operation of AZ31 magnesium

Original Article

aeronautical, automobile, biomedical and aerospace sectors. The use of certain materials, such as magnesium alloys

alloy. For the optimum condition of micro-drilling operation, the tool diameter, spindle speed and feed rate are taken as process parameters and material removal rate, circularity error are chosen as response parameters. The experimental results indicate that the multi-response characteristics of the AZ31 Magnesium alloys in drilling, the drilling process can be enhanced through the Gray Relational Analysis (GRA) method. Analysis of variance was carried out to determine the significant factors for the Drilling process. KEYWORDS: AZ31 Magnesium alloy, Circularity Error, Material Removal Rate, GRA, ANOVA

1. INTRODUCTION Nowadays, Magnesium alloys are used in a wide range of engineering and structural applications such as automotive, aeronautics, medicine, sports, biomedical science [1] and consumer electronics due to their high specific strength, high specific stiffness, excellent castability and high electromagnetic shielding [2-4]. The use of magnesium alloys in the European automobile industry encompasses parts such as steering wheels, steering column parts, instrument panels, seats, gear boxes, air intake systems, stretcher, gearbox housings, tank covers etc. The growth rate over the next 10 years has been forecast to be 7% per annum [5]. Machining is a highly complex mechanical process involving many variables with many options employed to investigate machining performance. During machining tests, carried at different speeds and feeds the formation of flank built-up on the cutting edges and margins of twist drills was typically observed in magnesium alloys [6]. In order to cope up with the

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A. Tajdeen, A. Megalingam, M. Kumar & B. Selva Kumar

inconvenience of reactivity in magnesium alloys leads to the risk of the firing of fine particles. So machining has been limited and usually, water-based lubricants are used [7]. Drilling is one of the fundamental machining processes of making holes, cylindrical and circular features are fundamental geometric features in hole making. As precision requirement becomes more stringent, it is not sufficient to consider the only size of circular and cylindrical parts along with their tolerances. The cylindrical conditions typically exhibit errors from the ideal design specifications in both the axial and radial directions [8]. Like aluminum alloys, chips formed during the dry drilling of magnesium alloys can readily adhere to the cutting tools and dry drilling in an AM60 magnesium alloy using both HSS and TiN-coated HSS drills, and observed that all the drills exhibited short tool lives— failing as a result of extensive magnesium adhesion to their cutting edges and drill flutes [9]. Drilling effect of circularity, cylindricity, surface roughness and hole oversize on drilling operation in Inconel 738-LC work-pieces were investigated. Drilling problems can account to expensive production waste because many drilling operations are usually among the final stages in manufacturing a part. There are the number of a problem occurring in the drilling process and also being faced by various industries in drilling include unwanted thrust force which leads to reduced drill life, drill fracture, burr formation in metals, temperature effect leads to lower material removal rate and vibration that affect surface finish and dimensional accuracy. Thus, the hole quality becomes a serious concern and surface roughness have a significant effect on fatigue life. Conventional machining of super alloys is difficult due to lower material removal rate, higher thrust force. Due to higher thrust force, it will lead to vibration and temperature which leads to lower surface finish [10]. Experiment on OHNS steel by an L18 orthogonal array of Taguchi’s method to analyze the effect of drilling parameters such as cutting speed, feed and drill tool diameter on surface roughness, tool wear by weight, material removal rate and hole diameter error using HSS spiral drill. Orthogonal arrays of Taguchi, the Signal–to- Noise (S/N) ratio, the analysis of variance (ANOVA), and regression analysis are employed to analyze the effect of drilling parameters on the quality of drilled holes. [11]

2. EXPERIMENTAL PROCEDURE 2.1 Work Piece Material The work material used for the study is Magnesium AZ31 alloy, which consists of Magnesium, Aluminum, Zinc and other elements with the following composition as tabulated in Table1. The Magnesium alloy sheet used here is of 20mm thickness. Table 1: Chemical Composition of AZ31 Magnesium AZ31 Al Zn Mn Si Cu Ca Fe Ni Mg

Impact Factor (JCC): 7.6197

Weight % 2.5 - 3.5 0.7 - 1.3 0.2 min 0.05 max 0.05 max 0.04 max 0.005 max 0.005 max Balance


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Multiresponse Optimization of Drilling Process Parameters of AZ 31 Magnesium Alloys Using Gray Relational Analysis Technique

2.2 Tool Material Tool material has a significant impact on the quality of hole produced as the size of tool materials have different wear resistance thus the quality of hole produced has the different surface finish. The drills were used in this investigation HSS with carbide coating with different diameters. 2.3 Design of Experiments Drilling experiments were carried out on a 20mm thick AZ31 Magnesium alloy with help above mentioned drill bits under different cutting conditions. The diameter of drill bits used to conduct the drilling tests was 6mm, 7mm, and 8mm. Tests were carried out under different spindle speed (rpm) and feed rates (mm/rev) [12]. As shown in figure 1, for the tests an MTAB CNC drilling machine speed range (150-6000 rpm) was utilized.

Figure 1: Experimental Set up of Vertical CNC Table 2: Levels and Factors Factors Spindle speed, N (rpm) Feed rate,f (mm/rev) Drill Size (mm)

1 2000 1.5 6

Levels 2 2500 1.8 7

3 3000 1.8 8

Experiments were conducted based on Taguchi’s L9orthogonal array as shown in Table 3. Taguchi’s process uses means to normalize the functions. “Signal-to-noise (S/N) ratio” reduces the variation in the output responses obtained from the experimental data by identifying the characteristic as “higher the better (HB), lower the better (LB), and nominal the best (NB).” Following equations are used to evaluate the type of performance characteristic using S/N ratio: HB: S = − 1 0 lo g  1  N



∑ n

LB: S = − 1 0 l o g  1  N

 n

∑

n i =1

n i=1

1   y2 

(1)

 y2  

(2)

2.4 Circularity Error Circularity is one of the most important parameters to check hole quality performance. It is defined as a twodimensional geometric tolerance that controls how much a feature can deviate from a perfect circle. Measurement of

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circularity is done by Mitutoyo CRT-A C544 three-dimensional coordinate measuring machine (CMM). The accuracy of CMM is + 0.0003mm. Minimum 10 points were measured to obtain the circularity error values at a constant depth of the hole. 2.5 Material Removal Rate (MRR) The material removal rate (MRR) in drilling is the volume of material removed by the drill per unit time. For a drill with a diameter D, the cross-sectional area of the drilled hole is πD2/4. The velocity of the drill perpendicular to the workpiece f is the product of the feed fr and the rotational speed N, Where N = V /π D. Thus, M RR =

π D

2

4

fr

mm3 /min

(3)

Table 3: L9 Experimental Design with Response Variables Speed (rpm) (A) 2000 2500 3000 2000 2500 3000 2000 2500 3000

S.No 1. 2 3. 4. 5. 6. 7. 8. 9.

Input Variables Feed Rate Drill Size (mm) (mm/rev) (C) (B) 1.8 6 2.1 7 2.4 8 1.8 8 2.1 7 2.4 6 1.8 8 2.1 6 2.4 7

Response Parameters Circularity MRR error 3 (mm /min) (µm) 998.63 10.33 1413.72 9.6 1570.79 9 1229.31 8.65 1811.31 7.78 1524.00 25 1379.23 9.6 1638.00 8.2 1599.00 10.9

3. RESULT AND DISCUSSIONS 3.1 Grey Relational Approach (GRA) GRA has been developed by Deng in 1982, to analyze the uncertainties in systems and relations between systems. GRA is a multi-objective optimization technique that converts multi-response into the single objective problem. Grey relational technique was developed to resolve problems using complex interrelationships among the multiple performance characteristics. In gray theory, the system has white, where the complete information is known and black, where the complete information is unknown, a system in Gray is implies deprived, incomplete, and ensure information about the performance characteristic. As per GRA procedure, the first four steps describe how to conduct the experiments, and the remaining steps are briefed below [14, 15]. 3.1.1 Normalization of S/N ratio In normalization, the raw experimental data are converted into zeroes and ones. The S/N ratios obtained from the Eqs. (4) and (5) have been normalized using the following relations. MRR corresponding to “HB” criterion can be expressed as

x i* ( k ) =

y i ( k ) − m in y i ( k ) m a x y i ( k ) − m in y i ( k )


(4)


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Circularity error corresponding to “LB” criterion can be expressed as

x i* ( k ) =

m ax y i ( k ) − y i ( k ) m a x y i ( k ) − m in y i ( k )

(5)

Where, i = 1, …, m, k = 1, 2, 3, …, n, m = no. of trial data in the experiment, n = no. of factors in drilling experiment, yi(k) = original sequence, xi(k) value after GRG, Min yi(k) and max yi(k) are the minimum and maximum value of yi(k) respectively. 3.1.2 Determination of deviation sequences,

∆ oi ( k )

From the below equation 6 deviation sequences are calculated.

∆ o i ( k ) = | x o* ( k ) − x i* ( k ) |

(6)

3.1.3. Determination of Grey Relational Coefficient (GRC) Calculation of Grey relational coefficient (GRC) is used to identify the relationship between the ideal and actual normalized results. If the two sequences have the same results, compared with all sequences, then their GRC is 1. The GRC can be calculated by using equation 7. The table 4 and 5 shows the calculated GRC of MRR and Circularity respectively.

γ ( x o ( k ), x i ( k )) =

∆ m in + ζ ∆ m a x ∆ o i ( k ) + ζ ∆ m ax

(7)

f is usually considered as 0.5 (distinguishing coefficient). Table 4: Grey Relational Coefficients for MRR Grey Relational Coefficients for MRR Level A B C 1 0.5676336 0.643585 0.541822706 2 0.400955339 0.341578 0.439554639 3 0.369027118 0.352453 0.356238712 Delta 0.198606482 0.291131 0.08331593 Rank 2 1 3 Average grey relational coefficient 0.148624006 Table 5: Grey Relational Coefficients for Circularity Error Grey Relational Coefficients for Circularity Error Level A B C 1 0.572944 0.969691 0.61571 2 0.601472 0.476633 0.61932 3 0.657162 0.385254 0.59655

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Delta 0.084218 0.584437 Rank 2 1 Average grey relational coefficient

0.02277 3 0.20351

3.1.4. Determination of Grey Relational Grade (GRG) After calculating the average sum of GRCs the Grey relational grade (GRG) has been evolved. GRG is the furthermost important consideration for evaluating multi-objective characteristics and it can be calculated through Equation 8.

γ ( xo , xi ) =

1 m

m

∑ γ (x i =1

o

( k ), x i ( k )) (8)

3.1.5 Finding the Optimal Parameters The high value of grey relational grade indicates the stronger relational degree between ideal sequence and present sequence. The ideal sequence is the best response in the machining process. The Higher grey relational grade indicates closeness to the optimal response in the process. At this stage, the best quality characteristic—larger-the-better—has been used for further analysis, as it gives the best performance of all the processes. 3.2 Analysis of Variance (ANOVA) Analysis of variance (ANOVA) is conducted to determine the significance level of each input parameters affecting the Multiple responses to drilling. The GRG obtained is analyzed by ANOVA, and it provides the statistical significance of the input process parameter over an output, and their results are shown in table 6 and 7 shows un-pooled and pooled ANOVA. Table 6: Un-pooled ANOVA Source of Variation

Sum of Squares

Degrees of Freedom

Mean Square

F value Calculated

A B C Error Total

0.020 0.120 0.004 0.002 0.146

2 2 2 2 8

0.010 0.060 0.002 0.001

10.491 61.526 2.016

F value % of (From Table) Inference Contribution Alpha=0.10 9 14.16 Significant 9 83.14 Significant 9 2.70 Insignificant 100

Table 7: Pooled ANOVA Source of Variation

Sum of Squares

Degrees of Freedom

Mean Square

A B Error Total

0.020 0.120 0.006 0.146

2 2 4 8

0.010 0.060 0.001 0.018

F value F Value % of (From Table) Calculated Contribution Alpha=0.10 6.958 6.94 14.57 40.804 6.94 85.43 0.080

Inference Significant Significant

In this drilling study, confidence level was taken as 90%. From the ANOVA tables, a feed rate is the most influencing factor for MRR and circularity error. The figure 2 Shows the influence of input parameters on the output



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responses in GRA. The table 8 shows the optimized values.

Figure 2: The Influence of Input Parameters on the Output Responses in GRA Table 8: GRG Table Level 1 2 3 Delta Rank

A(Spindle speed) 0.559367584 0.449035739 0.537002668 0.110331844 2 Avg.GRG

B(Feed rate) 0.67419 0.46689 0.40433 0.26986 1 0.51514

C(Drill Size) 0.5069628 0.4946547 0.5437885 0.0491338 3

4. CONCLUSIONS In this work, AZ31magnesium alloy is machined in a CNC drilling machine as per L9 orthogonal array. Material removal rate and circularity of drill hole have been measured for all workpieces through multi-response optimization of GRA technique. The important conclusions drawn from the present research are summarized as follows. •

The material removal rate and circularity could be effectively predicted by using different size of Carbide coated HSS cutting tools and Spindle speed, feed rate as the input parameters.

•

From GRG value, ANOVA indicated that considering the individual parameters, feed rate has been found to be the most influencing parameter followed by speed and Drill size on the overall objective.

•

The Optimized parameter combinations obtained using GRA were spindle Speed = 1.8mm/rev, drill size = 8mm and the corresponding output values were Material Removal rate (MRR) = 1229.31mm3/min and circularity error = 8.65 µm.

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