Optimization of Face Milling Parameters on Surface Roughness of SAE 1541

Optimization of Face Milling Parameters on Surface Roughness of SAE 1541

Sarang S Kulkarni*,Prof. M. G. Rathi**

*(Research Scholar Post graduate Student, Mechanical Engineering Department, Government College of Engineering,Aurangabad,Maharashtra,431005)

**(Asst. Professor, Mechanical Engineering Department ,Government College of Engineering,Aurangabad,Maharashtra,431005) Dr.BabasahebAmbedkarMarathwada University Aurangabad, India

Abstract-The aim of this study is limited to optimise surface roughness of SAE 1541 material.This is done by optimising face milling process parameters like cutting speed,feed,depth of cut and coolant flow rate.The setting of experiment is done by Design of Experiment (DOE) by Taguchi technique, S/N ratio, ANOVA. Finally effects of process parameters on surface roughness (Ra) in face milling studied and optimum solution is carried out. Mathematical model is generated by Regression Analysis and it is validated by confirmation tests. Thus, it is shown that, the Taguchi method is best suitable to solve surface quality problems on SAE 1541.

Keywords- FaceMilling, optimisation, Surface Roughness Taguchi Method,L9

OrthogonalArray,Cuttingspeed,feed,depth of cut,coolant flow rate,ANOVA

1. INTRODUCTION

   With more demands of modern engineering products,the control of surface roughness has become more important.In manufacturing industries,manufacturing focuses on productivity and quality of the product.To increase production computer numerical control (CNC) and vertical machining centres (VMC) machining tools are implemented during past decades[1].

   Face milling is an operation for producing plane or flat surfaces using face milling cutters. Surface roughness is one of the most important parameter to determine the quality of product. Several factors like cutting speed, feed, depth of cut, type of coolant, coolant pressure, tool geometry, work piece material influence surface roughness[2].

   a) Surface roughness and measurement

   Surface roughness of a machined product could affect several of the products functional attributes, such as contact causing surface friction, wearing, light reection, heat transmission, ability of distributing and holding a lubricant, coating, and resisting fatigue[3]. There are several ways to describe surface rough- ness. One of them

   is average roughness which is often quoted as Ra symbol. Ra is dened as the arithmetic value of the departure of the prole from the center-line along sampling [4].

   There are many methods of measuring surface roughness, such as using specimen blocks by eye or ngertip, microscopes, stylus type instruments, prole tracing instruments, etc. The tools measuring surface roughness withprobes, measure, and control in appropriate length and circumferences.The probe comes in and out holes while traveling on the surface. The probe is made up of diamond nip which very high in cost [5].

2. EXPERIMENTAL DETAILS

   In this work,Taguchi method used for Design of experiments (DOE).Analysis of Variance is adapted for performance analysis.Finally,with the use of Regression Analysis,mathematical model for Ra is Generated and it is validated by confirmation test.

   1. Design of Experiment

      Taguchi and Konishi had developed Taguchi techniques [6]. These techniques have been utilized widely in engineering analysis to optimize the performance characteristics within the combination of design parameters. Taguchi technique is also power tool for the design of high quality systems. It introduces an integrated approach that is simple and efficient to find the best range of designs for quality, performance, and computational cost [7].In this study we have consider 4 factors which affect majorly on quality characteristic such as (A) Cutting Speed

      , (B) Feed ,(C) Depth of Cut and (D) Coolant Flow Rate. The design of experiment was carried out by Taguchi methodology using Minitab 14 software. In this technique the main objective is to optimize the surface roughness that is influenced by four input process parameters.

   2. Taguchi Method

      Characteristics of Taguchi Technique are as follows: a)simple, efficient and systematic method to

      optimize product/process to improve the performance or reduce the cost.

      1. Help arrive at the best parameters for the optimal conditions with the least number of analytical investigations.

      2. It is a scientifically disciplined mechanism for evaluating and implementing improvements in products, processes, materials, equipments and facilities.

      3. Therefore, the Taguchi method has great potential in the area of low cost experimentation. Thus it becomes an attractive and widely accepted tool to engineers and

         scientists [8].

         Determine suitable working levels of the design factors

         Select proper Orthogonal Array (OA)

         Run experiments

         Analyse data

         Identify optimum condition

         Confirmation runs

         Determine the results of parameter design by tightening the tolerance of the significant factors

         Fig1.Flow chart of Taguchi method

         1. Characteristic Nominal is better S/N = -10 log (/s2y)(i)

         2. Characteristic Smaller is better

            S/N = -10 log ((l/n) (y2))………….(ii)

         3. Characteristic Larger is better

         S/N = -10 log ((1/n) ( (1/y2))………(iii)

         Where; is the average of data observation, s²y is the variance of y, n the number of observations and y is the observed data. The characteristic used in this experiment was the S/N ratio smaller is better, where the low surface roughness is desirable [9].

         In the Taguchi method, the term signal represents the desirable value (mean) for the output characteristic and the term noise represents the undesirable value for the output characteristic. Taguchi uses the S/N ratio to measure the quality characteristic deviating from the desired value. There are several S/N ratios available depending on type of characteristic: lower is better (LB), nominal is best (NB), or larger is better (LB).Smaller is better S/N ratio used here because the quality characteristic is Surface Roughness Ra.Smaller-the- better quality characteristic was implemented and introduced in this study.

   3. Workpiece Material

      In this study,workpiece material is SAE 1541,a type of low alloy steel. It is also called as medium carbon steels is type of Mnalloy[10]. Percentage of each element is given below:

      Table 1: Chemical composition of work-piece component

      C	Mn	Si	Cr	Al	Cu	HRC
      0.399	1.52	0.205	0.103	0.25	0.15	52-59

   4. Working Machine

      For the experiments,Hyundai WIA F500DI model Vertical Machining Centre (VMC) is used.

      Fig 2.Working Machine Hyundai WIA F500DI

      High performance HYNDAI WIA f 500D CNC Milling machine (working space, X, Y and Z movements being 600×460×570 mm) variable spindle speeds, optimum 8000 rpm; main spindle power,14.7 kw.having table size 700×500mm was employed to perform experiments.

   5. Surface Roughness Tester

   Mititoyo surface tester of model SJ-400 was used to measure surface roughness in experimental work.The roughness tester having measuring force 075mN-4mN and Diamond tip 5µm stylus having accuracy ±0.03µm.The probe comes in and out holes while traveling on te surface. The probe is made up of diamond nip which very high in cost

   Fig 3.Surface roughness testerMititoyo SJ-400

3. EXPERIMENTAL CONDITIONS

   A series of experiments were carried out on Hyundai WIA F500DI (VMC) (Sanjeev Auto). From OVAT analysis four input controlling parameters selected having three levels.

   Sr. No	Process Parameters	Level I	Level II	Level III
   1	Cutting Speed (RPM)	1200	1400	1600
   2	Feed (mm/min)	150	180	210
   3	Depth of Cut (mm)	0.2	0.3	0.4
   4	Coolant Flow Rate (lit/min)	20	40	60

   Table 2:Summary details of parameters and their levels

   Table 3: Experimental design matrix of L9 Orthogonal Array by Minitab 14

   Exp No	A Speed (RPM)	B Feed (mm/ min)	C Depth Of Cut (mm)	D Coolant Flow Rate (Lit/min)
   1	1200	150	0.2	20
   2	1200	180	0.3	40
   3	1200	210	0.4	60
   4	1400	150	0.3	60
   5	1400	180	0.4	20
   6	1400	210	0.2	40
   7	1600	150	0.4	40
   8	1600	180	0.2	60
   9	1600	210	0.3	20
   10	1200	150	0.2	20
   11	1200	180	0.3	40
   12	1200	210	0.4	60
   13	1400	150	0.3	60
   14	1400	180	0.4	20
   15	1400	210	0.2	40
   16	1600	150	0.4	40
   17	1600	180	0.2	60
   18	1600	210	0.3	20

4. RESULTS AND ANALYSIS

   A table is got after actual experimentation, showing Ra values and S/N ratios for each trials.Table is given below:

   Table 4:Summary Report for different trial conducted during Experimentation

   Exp No	Speed	Feed	DOC	CF	Ra	S/N Ratio
   1	1200	150	0.2	20	0.69	3.223
   2	1200	180	0.3	40	1.07	-0.5876
   3	1200	210	0.4	60	1.48	-3.4052
   4	1400	150	0.3	60	0.75	2.4987
   5	1400	180	0.4	20	1.01	-0.0864
   6	1400	210	0.2	40	1.14	-1.138
   7	1600	150	0.4	40	0.61	4.2934
   8	1600	180	0.2	60	0.63	4.0131
   9	1600	210	0.3	20	0.88	1.1103
   10	1200	150	0.2	20	0.88	1.1103
   11	1200	180	0.3	40	1.06	-0.5061

   12	1200	210	0.4	60	1.47	-3.3463
   13	1400	150	0.3	60	0.84	1.5144
   14	1400	180	0.4	20	0.86	1.31
   15	1400	210	0.2	40	1.01	-0.0864
   16	1600	150	0.4	40	0.59	4.5829
   17	1600	180	0.2	60	0.61	4.2934
   18	1600	210	0.3	20	0.91	0.8191

   1. S/N Ratio Analysis-

      Table 5: Response Table for Signal to Noise Ratios – Smaller is better (Ra)

      Level	Speed	Feed	DOC	CF
      1	-0.6278	2.8180	1.8488	1.1859
      2	0.6302	1.3866	0.7980	1.0816
      3	3.1830	-1.0192	0.5386	0.9180
      Delta	3.8109	3.8372	1.3102	0.2679
      Rank	2	1	3	4

      Table 6: Response Tables for Means

      Level	Speed	Feed	DOC	CF
      1	1.1083	0.7267	0.8267	0.8717
      2	0.9350	0.8733	0.9183	0.9133
      3	0.7050	1.1483	1.0033	0.9633
      Delta	0.4033	0.4217	0.1767	0.0917
      Rank	2	1	3	4

      The level of a factor with the highest S/N ratio was the optimum level for responses measured. From the Table 7 and Figure 4 it is clear that, the optimum value levels for better surface finish (Ra) are at a cutting speed (1600 rpm), feed (150 mm/min),depth of cut (0.2mm) and coolant flow rate (20 lit/min). The response table includes ranks based on Delta statistics, which compare the relative magnitude of effects. The Delta statistic is the highest minus the lowest average for each factor. Minitab assigns ranks based on Delta values[8]; rank one to the highest Delta value, rank two to the Second highest, and so on[11].

      From both ANOVA and response tables it is clear that the most significant factor is feed (B), followed by cutting speed (A), depth of cut (C) and coolant flow rate (D). Figure 4 shows graphically the effect of the four control factors on surface roughness (Ra).

      Main Effects Plot (data means) for SN ratios

      Speed Feed

      3

      2

      1

      0

      -1

      1200

      1400

      DOC

      1600

      150

      180

      CF

      210

      0.2

      0.3

      0.4

      20

      40

      60

      Signal-to-noise: Smaller is better

      3

      2

      1

      0

      -1

      Mean of SN ratios

      Mean of Means

      Fig 4. Effects of process parameters on S/N Ratios

      Main Effects Plot (data means) for Means

      Speed Feed

      1.1

      1.0

      0.9

      0.8

      0.7

      1200

      1400

      DOC

      1600

      150

      180

      CF

      210

      0.2

      0.3

      0.4

      20

      40

      60

      1.1

      1.0

      0.9

      0.8

      0.7

      Fig 5.Effects of process parameters on Means

   2. Analysis of Variance (ANOVA)

   The purpose of ANOVA is to investigate which process parameters significantly affect the quality characteristic. The analysis of the experimental data is carried out using the software MINITAB 14specially used for design of experiment applications. In order to find out statistical Significance of various factors like cutting speed (A), feed (B), depth of cut (C) and coolant flow rate (D) and their interactions on surface roughness (Ra), analysis of variance (ANOVA) is performed on experimental data[10]. Table 8 shows the result of the ANOVA with the surface roughness. The last column of the table indicates p-value for the individual control factors. It is known that smaller the p-value, greater the significance of the factor. The ANOVA table for S/N ratio (Table 7) indicate that, the cutting speed (p=0.000), feed (p= 0.000), depth of cut (p=0.019) and coolant flow rate (p=0.0737) in this order, are significant control factors affecting surface roughness.[11] It means, feed is the most influencing factor and the coolant flow rate has less influence on the performance output compared to cutting speed and depth

   Table 8:Analysis of Variance for Ra, using Sequential SS for Tests

   Source	DF	Seq SS	Adj SS	Adj MS	F	P
   Speed	2	0.49124	0.49124	0.24562	51.71	0.000
   Feed	2	0.54988	0.54988	0.27494	57.88	0.000
   DOC	2	0.09368	0.09368	0.04684	9.86	0.005
   CF	2	0.02528	0.02528	0.01264	2.66	0.124
   Error	9	0.04275	0.04275	0.00475
   Total	17	1.20283

   S = 0.0689202 R-Sq = 96.45%

   R-Sq(adj) = 93.29

   Residual Plots for SNRA1

   Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

   of cut.

   Table 7: Analysis of Variance for S/N Ratio, using adjusted SS for Tests

   99

   Percent

   90

   50

   10

   1

   -1.0

   -0.5

   0.0

   Residual

   0.5

   1.0

   1.0

   Residual

   0.5

   0.0

   -0.5

   -1.0

   -4

   -2 0 2 4

   Fitted Value

   Source	DF	Seq SS	Adj SS	Adj MS	F	P
   Speed	2	44.248	44.248	22.124	45.54	0.000
   Feed	2	46.023	46.023	23.011	47.36	0.000
   DOC	2	6.136	6.136	3.068	6.31	0.019
   CF	2	0.307	0.307	0.153	0.32	0.737
   Error	9	4.373	4.373	0.486
   Total	17	101.087

   Histogram of the Residuals Residuals Versus the Order of the Data

   4 1.0

   Frequency

   Residual

   3 0.5

   2 0.0

   1 -0.5

   0

   -1.0

   -0.5

   0.0

   0.5

   1.0

   -1.0

   2 4

   6 8 10

   12 14

   16 18

   Residual Observation Order

   S = 0.697035 R-Sq = 95.67%

   R-Sq(adj) = 91.83%

   Fig 6.Residual plots for S/N ratio of Ra

   1. Regression Analysis

      Mathematical models for process parameters such as cutting speed, feed, depth of cut and coolant flow rate were obtained from regression analysis using MINITAB 14 statistical software to predict surface Roughness

   2. Taguchi predicted values

      From graphs,obtained after resultsit is clear that at high S/N ratio low Ra value can be obtained.After selecting cutting speed at 1600 rpm,feed at 150 mm/min, depth of cut at 0.2 mm and coolant flow rate at 20 lit/min,predicted value by Taguchi as follows:

      Table 9: Predicted values by Taguchi method

      S/N Ratio	Mean	StDev	Log(StDev)
      5.85034	0.38166	0.1013	-2.16048

   3. Confirmation Test

   At cutting speed at 1600 rpm, feed at 150 mm/min, DOC at 0.2 mm and coolant flow rate at 20 lit/min trials were taken for confirmation.By this validation of mathematical model for Ra value made by Regression Analysis is validated.In this Experimental results in Table 4 and predicted values were compared and it is observed that

   % Error is varying in between 0.95% to 4.77%.Hence it is concluded that model is valid.

   Table 10: Confirmation Tests for predicted values by Taguchi method

   Sr No	Predicted Ra mean Value	Trials		Ra mean value	% Error
   		1	2
   1	0.3816	0.386	0.392	0.389	2.01%

   The results are shown in above table and it is observed that the average Ra i.e. 0.3894 µm and average S/N Ratio 5.8398 which falls within predicted 80% Confidence Interval.

5. CONCLUSIONS

   Certain conclusions are drawn out from the study are as follows:

   1. It is observed that,cutting speed,feed and depth of cut has significant effecton surface roughness.

   2. Coolant flow rate is the least significant parameter on surface roughness.

   3. Feed is the most significant parameter on surface roughness.

   4. The result of present investigation is valid within specified range of process parameters.

   5. Also the prediction made by Regression Analysis is in good agreement with Confirmation results.

   6. For better surface finish i.e low roughness value (Ra), higher cutting speed, lower feed rate, lower depth of cut and low coolant flow rate is essential for work piece of SAE 1541.

   7. The optimal levels of face milling process parameters are found to be:

      Table10:optimised levels of parameters for face milling operation

      Parameter	Value
      Cutting Speed (RPM)	1600
      Feed (mm/min)	150
      Depth of cut (mm)	0.2
      Coolant flow rate (lit/min)	20

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