OPTIMIZATION OF PROCESS PARAMETERS IN HOT MACHINING OF 15-5 PH STAINLESS STEEL USING TAGUCHI METHOD

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S. Gajanana, D. Chakradhar and N. Sai Alok Reddy ijesird, Vol. III, Issue III, September 2016/236

OPTIMIZATION OF PROCESS PARAMETERS IN HOT MACHINING

OF 15-5 PH STAINLESS STEEL USING TAGUCHI METHOD

S. Gajanana¹, D. Chakradhar², N. Sai Alok Reddy³

Department of Mechanical Engineering, MVSR Engineering College, Osmania University, Hyderabad, Telangana^1,3 Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, Karnataka, India².

1[email protected], ²[email protected], ³[email protected]

Abstract: - This paper envisages the optimization of machining parameters in hot machining using Taguchi Method.Experiments were conducted on 15-5 PH stainless steel using coated carbide tool based on Taguchi L₉ orthogonal array design. The work piece material was heated using oxy-acetylene gas flame. The effect of Speed, Feed and Depth of Cut onSurface Roughness, Tool Wear and Metal Removal Rate was found and optimum conditions were determined. Further Analysis of variance was performed to get the contribution of each parameter on the response and it was observed that cutting speed and feed most significantly affect the performance parameters compared to depth of cut.

Keywords— Hot Machining, Taguchi, 15-5 PH Steel, Surface roughness, MRR, Tool Wear.

I. INTRODUCTION

High strength work materials have tremendous applications in the field of aerospace, nuclear, biomedical, automotive, etc. It is a challenging task to machine these high strength materials. Although there have been many methods evolved to machine such materials, such techniques are expensive and costly cutting tools are required to machine those materials. Hot machining is a process in which workpiece has to be heated below recrystallization temperature but in some cases it has been also heated above recrystallization temperature. High Manganese steel and other high wear resistance alloys which are widely used for various applications are having high strain hardening property. The hot machining operation is based on the softening phenomenon at the vicinity of shear zone (deformation zone). Softening of workpiece at the deformation zone makes the material ductile (reduces shear strength) which helps to reduce cutting force and increment in surface integrity.

Heating gas flame used for operation should be in a constant manner, which delivers same temperature throughout the workpiece material. Heating can be done prior or at the time of machining. The blowpipe direction should be opposite to tool holder for better heating. There are many controlling factors such as workpiece temperature, cutting speed, feed rate, depth of cut, nose radius, cutting time, etc. which contribute on the performance characteristics. The problem arises may be due to the use of incorrect levels of control parameters such as feed, depth of cut and cutting velocity, etc.

Tool life and power consumption have much contribution in cost of manufacturing. Surface finish is the most desired characteristic for good performance of product. Chip reduction coefficient is also an effective measure which evaluates the machinability. The appropriate selection of machining parameters has to be made to achieve the above machinability criteria. The heat requirements for this process should satisfy the following conditions

1. Heat input rate should be very high such that the work piece gets heated up in a very short time.

2. The heat generated should heat only the shear zone.

3. Constant temperatures over a wide range should be generated.

4. The installation cost and operating cost should be less.

Based on the above requirements Oxy- Acetylene gas flame was used in the conducted experiments of Hot Machining.

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PreviouslyShah and Gelot [1] have presented a review on the hot machining operation and mentioned application for hot machining operation.

They mentioned importance to study the temperature at chip/tool interface. Dawami and Zadshakoyan [2] have conducted experiment on AISI 1060(45 HRC) material with uncoated TNNM 120608 SP10 tool by keeping all the cutting parameters constant. He analysed better surface finish at 300 °C compared to machining at room temperature. Secondly, they analysed the temperature variation on tool by varying the cutting speed at 300 °C and at room temperature. Baili et al. [3] have applied induction heating in hot machining operation for heating Ti-5553 material for reducing mechanical properties which reduces cutting forces. They focused to enhance the machinability of Ti-5553. He observed that there is 13% reduction in cutting force at 500°C where as it reduces to 34% at 700°C. Madhavulu and Basheer Ahmed [4] investigated hot machining operation using plasma as heat source. Stainless steel 410 and other alloys are considered as work material.

Ranganathan et al. [5] investigated the influence of cutting parameters using Taguchi technique on tool wear for AISI 316 stainless steel at 200°C, 400°C and at 600°C. They found different parameters are significant at different levels of temperature. At 200°C cutting speed and depth of cut are significant factors, at 400°C feed and depth of cut are significant and at 600C cutting speed and depth of cur are significant. They found low value of error at 200°C and at 400°C and high value for R2 shows significance of ANOVA table for optimization.

Ranganathan and Senthilvelan [6] conducted experiment using Taguchi design on stainless steel 316 for tool wear by varying temperature keeping other at constant level. Interaction between input factors has been discussed for tool wear using orthogonal array and ANOVA test. Regression equation has been developed for establishing the relationship between input variables and tool wear.

Maity and Swain [7] used half factorial design for conducting experiments for tool life using carbide cutting tool on high manganese steel. Expression for tool life has been established from statistical technique. They concluded that the temperature is

the most significant factor followed by cutting velocity, feed and depth of cut for tool life. L. Ozler et al. [8] applied factorial regression analysis for accomplishing the experimental work for tool life of sintered carbide inserts with austenitic manganese steel. It was noticed that as the cutting speed increased tool life decreased. At 600 °C surface temperature, tool life was found maximum as the workpiece material became ductile. It was noticed that as the feed rate increased tool life decreased.

Masood et al. [9] adopted laser heating technique to turn HCWCI using CBN and highlighted the decrement in cutting force (24 %) and thrust force (22 %).Masood et al. [9] worked on the similar kind of metal using laser heat and underlined some limitations of this process. Hence, there is a need to use an alternate heating method in machining such materials. Flame heating workpiece was introduced newly to overcome the limitations stated above.

However, optimization of this process is very important in order to reduce the manufacturing cost, improve the quality of the product, and reduce the lead time. Therefore, some of the analytical methods like Taguchi, analysis of variance (ANOVA), and artificial neural network (ANN) were used in this work. Various researchers have implemented these methods to optimize their TEM processes. Maity and Swain [7] adopted Taguchi technique to carry out the experiments on high- manganese steel with the assistance of oxy-liquid petroleum gas (LPG) flame and derived tool life equation using linear regression. Their result confirms both cutting force and thrust force increase with the increase of federate, and continuous chips are obtained at higher temperatures and discontinuous at lower temperatures. The work of Tosum and Ozler [10, 11] on the same material using oxy-LPG flame details that preheat increases the tool life by 2.45 times and decreases surface roughness by 2.34 times for the optimum input parameters. They used Taguchi method to carry out the experiments and analysed the multiple performance characteristics of the process using ANOVA. Tsao [12] also used the methods to predict the thrust force of the step drill in the drilling of composite materials and correlated

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the results with the experimental findings. He confirmed that ANN (error within 0.3 %) is a better predictive tool compared to linear regression model(errorwithin28%).In a similar kind of work, Tsao and Hocheng [13] used Taguchi and ANN techniques to evaluate the thrust force and surface roughness in the drilling of composite laminates and confirmed that ANN is more accurate than linear regression model. Sachin [14]used Taguchi and ANOVA to compare the tool lives of ceramic and CBN cutting tools when machining hardened steels (AISI 52100) and reported that cutting speed has significant effect on tool lives. In hard turning process, instead of opting costly tooling materials for softening of the workpiece, gas preheating could be an attractive alternative. This technique reduces the chances of work hardening and minimizes the resistance to cutting. Localized heating softens only the chip/cut material, leaving the workpiece relatively cool and metallurgically undamaged.

Though this method was first recognized by Tigham, Macy J.R., who owns a US patent, used electrical resistance to heat the workpiece and found success in his experiments. This encouraged other researchers to design different heating techniques and to investigate their usefulness in hard turning.

II. TAGUCHI METHOD

Definition:The technique of laying out the conditions of experiments [6] involving multiple factors was first proposed by the Englishman, Sir R.A.Fisher. The method is popularly known as the factorial design of experiments. A full factorial design will identify all possible combinations for a given set of factors. Since most industrial experiments usually involve a significant number of factors, a full factorial design results in a large number of experiments. To reduce the number of experiments to a practical level, only a small set from all the possibilities is selected. The method of selecting a limited number of experiments which produces the most information is known as a partial fraction experiment. Although this method is well known, there are no general guidelines for its application or the analysis of the results obtained by performing the experiments. Taguchi constructed a

special set of general design guidelines for factorial experiments that cover many applications. Taguchi has envisaged a new method of conducting the design of experiments which are based on well- defined guidelines. This method uses a special set of arrays called orthogonal arrays. These standard arrays stipulates the way of conducting the minimal number of experiments which could give the full information of all the factors that affect the performance parameter. The crux of the orthogonal array method lies in choosing the level combinations of the input design variables for each experiment.

A typical orthogonal array:While there are many standard orthogonal arrays available, each of the arrays is meant for a specific number of independent design variables and levels. For example, if one wants to conduct an experiment to understand the influence of 4 different independent variables with each variable having 3 set values (level values), then an L9 orthogonal array might be the right choice. The L9 orthogonal array is meant for understanding the effect of 4 independent factors each having 3 factor level values. This array assumes that there is no interaction between any two factors. While in many cases, no interaction model assumption is valid, there are some cases where there is a clear evidence of interaction. A typical case of interaction would be the interaction between the material properties and temperature.

III.EXPERIMENTAL INVESTIGATION

3.1 INTRODUCTION

Precipitated hardening (PH) stainless steels show excellent mechanical properties, excellent weldability and good corrosion resistance. That is why they are used in aerospace and nuclear industries. Predicting the surface integrity of mechanical parts is crucial for these industries. The 15-5PH steel has a martensitic microstructure. So during heating, austenization can occur if the temperature exceeds the transformation start temperature. Precipitate hardened stainless steels exhibit high strengths at temperature up to 310˚C similar to martensitic stainless steel. The costly cutting tool of high hardness is required to machine

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this material. By heating the material to elevated temperature, the material can easily be machined. In the present investigation, the experimental investigation of hot machining operation of 15-5 PH stainless steel has been carried out with gas flame heating using Taguchi method. The optimization of the process parameters has been carried out to enhance tool life, metal removal rate (MRR) and to reduce the tool wear, surface roughness.

Composition of the workpiece material is shown below

Table 1. Composition of 15-5 PH stainless steel.

Material %

Carbon 0.07

manganese 1.00

phosphorus 0.040

sulphur 0.030

silicon 1.00

chromium 14.00 – 15.50

nickel 1.5 – 5.50

Copper 2.5 – 4.5

Columbium +Tantalum

0.15 – 0.45

The details of the cutting tool are given in the table 2.

Table 2: Cutting tool dimensions

Rake angle -6˚

Clearance angle 0˚

Major cutting edge 75˚

Nose radius 8mm

Insert thickness 4mm

3.2 EXPERIMENTAL PROCEDURE

The experiments were accomplished on a three jaw chuck centre lathe machine at Machine Shop of NITK. The diameter and length of workpiece are taken as 32 mm and 150 mm respectively. The hardness of the workpiece material was measured to be 40 HRC. The insert SNMG 120408 was used as cutting tool for machining. The experiments were performed on lathe with different speeds, feeds and depth of cuts. Coated carbide insert as cutting tool and 15-5 PH stainless steel with 32mm diameter as workpiece are used in these experiments.

Experiments are conducted using oxyacetylene gas flame for heating the workpiece. It is one of the best choices for hot machining because it has low

equipment cost and heat transfer to the workpiece is very low. Metallurgical damage to the workpiece is very low.

3.2.1 Selection of the independent variables:

The initial cutting parameters were as follows:

cutting speed 224 rpm; feed rate 0.05 mm/ rev; and depth of cut 0.3 mm. The feasible space for the cutting parameters was deﬁned by varying the cutting speed in the range 224-775 rpm, the feed rate in the range 0.05-0.15 mm/rev, and the depth of cut in the range 0.3-0.9 mm. In the cutting parameter design, three levels of the cutting parameters were selected, shown in Table 3.

Table 3: Cutting parameters and their levels.

Symbol Parameter Units Level 1

Level 2

Level 3

A Speed Rpm 224 500 775

B Feed mm/rev 0.05 0.1 0.15

C Depth of

cut

mm 0.3 0.6 0.9

3.2.2 Deciding the number of levels

Once the independent variables are decided, the number of levels for each variable is decided.

The selection of number of levels depends on how the performance parameter is affected due to different level settings. In the present experiment the number of levels of each parameter are three.

3.2.3 Selection of an orthogonal array:

Before selecting the orthogonal array, the minimum number of experiments to be conducted shall be fixed based on the total number of degrees of freedom present in the study. The minimum number of experiments that must be run to study the factors shall be more than the total degrees of freedom available. For the present experiment the L9 array is selected and its layout is shown in table 4.

Table: 4 layout of L9 orthogonal array

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3.2.4 Assigning the independent variables to columns:

The order in which the independent variables are assigned to the vertical column is very essential. In case of mixed level variables and interaction between variables, the variables are to be assigned at right columns as stipulated by the orthogonal array. Minitab 17 software was used to assign the proper values to columns.

3.2.5 Conducting the experiment:

Once the orthogonal array is selected, the experiments are conducted as per the level combinations. It is necessary that all the experiments be conducted. The interaction columns and dummy variable columns shall not be considered for conducting the experiment, but are needed while analysing the data to understand the interaction effect.

Since turning operations are accomplished using a cutting tool, the high forces and temperature during machining create a very harsh environment for the cutting tool. Therefore, tool life is an important index to evaluate cutting performance in a turning operation. In addition, the purpose of turning operations is to produce a low surface roughness of the machined workpiece. Therefore, surface roughness is another important index to evaluate cutting performance. Basically, tool life and surface roughness correlated strongly with cutting parameters such as cutting speed, feed rate, and depth of cut. Proper selection of the cutting parameters can secure longer tool life and better surface roughness. Hence, design optimization of the cutting parameters based on the Taguchi method is adopted to improve the tool life and the surface roughness in a turning operation. The performance

parameters considered to measure are Surface Roughness, Metal Removal Rate and Tool wear.

Table 5: Experimental Results for Tool Wear (TW) S.NO SPEED FEED DOC TW S/N

RA1

MEAN1

1 224 0.05 0.3 0.01 40.00 0.01

2 224 0.10 0.6 0.03 30.45 0.03

3 224 0.15 0.9 0.06 24.43 0.06

4 500 0.05 0.6 0.07 23.09 0.07

5 500 0.10 0.9 0.07 23.09 0.07

6 500 0.15 0.3 0.08 21.93 0.08

7 775 0.05 0.9 0.09 20.91 0.09

8 775 0.10 0.3 0.10 20.00 0.1

9 775 0.15 0.6 0.11 19.17 0.11

Table 6: Experimental Results for Surface Roughness (Ra)

S.NO SPEED FEED DOC Ra S/N

RA2

MEAN 2

1 224 0.05 0.3 2.35 -7.42 2.35

2 224 0.10 0.6 2.82 -9.00 2.82

3 224 0.15 0.9 3.00 -9.54 3.00

4 500 0.05 0.6 1.98 -5.93 1.98

5 500 0.10 0.9 2.49 -7.92 2.49

6 500 0.15 0.3 2.75 -8.78 2.75

7 775 0.05 0.9 1.94 -5.75 1.94

8 775 0.10 0.3 2.16 -6.68 2.16

9 775 0.15 0.6 2.62 -8.36 2.62

Table 7: Experimental Results for Metal Removal Rate (MRR)

S.NO SPEED FEED DOC MRR S/N

RA3

MEAN 3

1 224 0.05 0.3 4.50 13.06 4.50

2 224 0.10 0.6 9.50 19.55 9.50

3 224 0.15 0.9 14.40 23.16 14.40

4 500 0.05 0.6 16.80 24.50 16.80

5 500 0.10 0.9 18.50 25.34 18.50

6 500 0.15 0.3 20.26 26.13 20.26

7 775 0.05 0.9 20.90 26.40 20.90

8 775 0.10 0.3 21.00 26.44 21.00

9 775 0.15 0.6 26.00 28.29 26.00

3.2.6 Analysis of the S/N ratio:

In the Taguchi method, the term ‘signal’ represents the desirable value (mean) for the output characteristic and the term ‘noise’ represents the undesirable value (S.D.) for the output characteristic. Therefore, the S/N ratio is the ratio of

parameter

1 1 1 1 P1

2 1 2 2 P2

3 1 3 3 P3

4 2 1 2 P4

5 2 2 3 P5

6 2 3 1 P6

7 3 1 3 P7

8 3 2 1 P8

9 3 3 2 P9

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the mean to the S.D. Taguchi uses the S/N ratio to measure the quality characteristic deviating from the desired value.

The S/N ratio h is deﬁned as

h=−10 log (M.S.D.) ……. (1) Where M.S.D. is the mean-square deviation for the output characteristic. There are three categories of quality characteristics, i.e. the-lower-the-better, the higher-the-better, and the-nominal-the-better. To obtain optimal cutting performance, the-higher-the- better quality characteristic for tool life must be taken. The mean-square deviation (M.S.D.) for the- higher-the-better quality characteristic can be expressed as:

2i………. (2)

Where m is the number of tests and T_i is the value of tool life and the i^th test. Table 7 shows the experimental results for MRR and the corresponding S/N ratio using equations (1) and (2).

Since the experimental design is orthogonal, it is then possible to separate out the effect of each cutting parameter at different levels. For example, the mean S/N ratio for the cutting speed at levels 1, 2 and 3 can be calculated by averaging the S/N ratios for the experiments 1–3, 4–6, and 7–9, respectively. The mean S/N ratio for each level of the other cutting parameters can be computed in the similar manner. The mean S/N ratio for each level of the cutting parameters is summarized and called the S/N response table. As shown in equations (1) and (2), the greater is the S/N ratio, the smaller is the variance of MRR around the desired (the- higher-the-better) value. However, the relative importance amongst the cutting parameters for MRR still needs to be known so that optimal combinations of the cutting parameter levels can be determined more accurately. This will be discussed in the next section using the analysis of variance.

On the other hand, the-lower-the-better quality characteristics for surface roughness and tool wear should be taken for obtaining optimal cutting performance. The M.S.D. for the-lower-the-better quality characteristic can be expressed as:

2

i ………….. (3)

Where S_i is the value of surface roughness for the i^th test. Table 5 & 6 shows the experimental results for surface roughness and the corresponding S/N ratio using equations (1) and (3). The S/N response table and S/N response graph for surface roughness are shown in Fig. 2. Regardless of the-lower-the-better or the higher-the-better quality characteristic, the greater S/N ratio corresponds to the smaller variance of the output characteristic around the desired value.

3.3 RESULTS AND DISCUSSIONS:

3.3.1 Analysis of variance:

The purpose of the analysis of variance (ANOVA) is to investigate which design parameters signiﬁcantly affect the quality characteristic. This is to be accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean S/N ratio, into contributions by each of the design parameters and the error. First, the total sum of squared deviations SST from the total mean S/N ratio can be calculated as:

SST= (Ƞi - Ƞm)²..…… (4) Where n is the number of experiments in the orthogonal array and hi is the mean S/N ratio for the i^th experiment. The total sum of spared deviations SST is decomposed into two sources: the sum of squared deviations SS_d due to each design parameter and the sum of squared error SSe. The percentage contribution r by each of the design parameters in the total sum of squared deviations SST is a ratio of the sum of squared deviations SSd due to each design parameter to the total sum of squared deviations SST. Statistically, there is a tool called an F test named after Fisher [12] to see which design parameters have a signiﬁcant effect on the quality characteristic. In performing the F test, the mean of squared deviations SS_m due to each design parameter needs to be calculated. The mean of squared deviations SSm is equal to the sum of squared deviations SSd divided by the number of degrees of freedom associated with the design

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parameter. Then, the F value for each design parameter is simply the ratio of the mean of squared deviations SSmto the mean of squared error.

Usually, when F\4, it means that the change of the design parameter has a signiﬁcant effect on the quality characteristic.

Table 8: ANOVA for Tool Wear

SOURCE DF Seq SS Adj SS Adj MS F

SPEED 2 0.006756 0.006756 0.003378 23.38 FEED 2 0.001089 0.001089 0.000544 3.77

DOC 2 0.000156 0.000156 0.000078 0.54 ERROR 2 0.000289 0.000289 0.000144 TOTAL 8 0.008289

Table 8 shows the results of ANOVA for tool wear.

It can be found that cutting speed and feed rate are the signiﬁcant cutting parameters for affecting tool wear. Therefore, based on the S/N and ANOVA analyses, the optimal cutting parameters for maximum tool wear are the cutting speed at level 3, the feed rate at level 3, and the depth of cut

at level 3.

Fig 1: S/N Plot for Tool Wear

Table 9 the results of ANOVA for surface roughness. Cutting speed, feed rate, and depth of cut are the signiﬁcant cutting parameters for affecting surface roughness. However, the contribution order of the cutting parameters for surface roughness is feed rate, then depth of cut, and then cutting speed. The optimal cutting parameters for surface roughness are the cutting speed at level 1, the feed rate at level 3, and the depth of cut at level 2.

Fig 2: S/N Plot for Surface Roughness

Table 9: ANOVA for Surface Roughness

By the above analysis, optimum conditions for

metal removal rate can be found using a similar technique but the signal-to-noise ratio will be Larger is better type.

The optimum conditions for maximum metal removal rate are cutting speed at level 3, feed rate at level 3, depth of cut at level 3. Table 10 shows the results of ANOVA for MRR. The contribution order of the cutting parameters for Metal Removal Rate are feed, speed and depth of cut. Fig 3 shows the SN plot for the metal removal rate.

Table 10: ANOVA for Metal Removal Rate

SOURCE DF Seq SS Adj SS Adj MS F % SPEED 2 0.36167 0.361667 0.180833 13.46 32.6

FEED 2 0.74000 0.740000 0.370000 27.54 66.8 DOC 2 0.00607 0.006067 0.003033 0.23 0.5 ERROR 2 0.02687 0.026867 0.013433

TOTAL 8 1.13460

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S. Gajanana, D. Chakradhar and N. Sai Alok Reddy ijesird, Vol. III, Issue III, September 2016/243 Fig 3: S/N Plot for Metal Removal Rate

3.4 CONFORMATION TESTS

Once the optimal level of the design parameters has been selected, the ﬁnal step is to predict and verify the improvement of the quality characteristic using the optimal level of the design parameters. The estimated S/N ratio h ˆ using the optimal level of the design parameters can be calculated as:

ĥ = µ_m + (µ_{i -}µ_m)

Where µm is the total mean S/N ratio, µi is the mean S/N ratio at the optimal level, and o is the number of the main design parameters that affect the quality characteristic.

IV.CONCLUSION

The following conclusions have been derived by applying Taguchi Method in hot machining of 15-5 PH Stainless steel.

 Preheating of the material decreases the strain hardness and increases flow stresses in it; hence, easy shearing of material can occur. Also, it improves the tool life and surface finish of the workpiece at optimum surface temperature ranges.

 The experimental results show that tool wear, surface roughness decreases and metal removal rate increases by small amount with increase in temperature of workpiece in heated condition.

 From the Analysis of Variance it is evident that speed is the major contributing factor for tool wear (84.4%) and metal removal rate (79.4%) and feed is major contributing factor for surface roughness (66.8%).

 Cutting speed, feed rate and depth of cut are the major factors that affect the quality of hot machining of 15-5PH stainless steel, while workpiece temperature is considered as secondary factor.

V.REFERENCES

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SPEED 2 272.243 272.243 136.122 159.44 79.4 FEED 2 58.107 58.107 29.054 34.03 16.9

DOC 2 12.185 12.185 6.092 7.14 3.56

ERROR 2 1.707 1.707 0.854 TOTAL 8 344.243

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