Conventionally And Cryogenic Treated T42 Hss Biology Essay

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Abstract: Tool life is defined as machining time elapsed between two consecutive resharpenings. Cutting speed is one of the important factors affecting tool life, as it generates more heat, resulting in higher temperature of cutting zone. The wear of the tool increases as temperature of cutting zone increases. Tool wear is taken as one of the factor to evaluate tool life. As tool wear increases tool life decreases. In case of turning operation feed and depth of cut also play a role in increasing cutting zone temperature, thereby increasing wear, reducing tool life. An empirical mathematical model to evaluate wear of a conventionally treated T42 HSS tools w .r. t. turning process parameters is established. In order to enhance tool life, it has become need of the time to develop more and more wear resistant tool materials or different treatments on existing tool materials. Cryogenic treatment has proved to be one such treatment, which improves, wear resistance of tool steels. In present experimentation T42 HSS samples were subjected to cryogenic treatment at different temperatures and the tool samples were used for machining work pieces with same turning process parameters as used with conventionally treated tools. Taguchi's OA technique was used for deciding combination of parameters for experimentations carried out with both the conventionally and cryogenically treated tools. A significant improvement in wear resistance of cryogenically treated samples is noted. Empirical mathematical models to evaluate wear of conventionally and cryogenically treated T42 HSS is an outcome of this investigation, which was validated by carrying out confirmation experiments.

Keywords: Tool steel, Taguchi OA, empirical mathematical model, cryogenic treatment, improvement in wear resistance, nomogaph

1. INTRODUCTION

Tool life is defined as machining time elapsed between two consecutive resharpenings Improvement in tool life plays a vital role in reducing the cost of production. Cutting tools fail either by a gradual and progressive wearing of its edge or due to chipping or plastic deformation. Usually the tool life criterion is defined as a predetermined threshold value of cutting tool wear. Clearly, any development in tool or work material increasing tool life is going to be beneficial to the industries. Continued efforts are being made to find different ways of improving life of the cutting tools.

Tool wear is one of the criterions, which can be used for deciding life of a tool. The types of tool wears are face (crater) wear, flank wear and nose wear. Almost all types of wears and the resulting recession of cutting edge affect on the dimensions, quality of work piece to a large extent [1]. Flank wear is the most commonly used criterion for evaluating tool life of HSS tools. In case of flank wear limiting value of 0.3 mm average and max value 0.6 mm, if reached is taken to be as end of tool life and the cutting edge is required to be re sharpened [2]. Flank wear of the tool is affected by tool material, work material, cutting fluid, tool geometry, process parameters and also by various treatments provided to the tools. When tool-work material, cutting fluid, tool geometry, machine and all other parameters except process parameters and treatments given to the tool material, are same then tool wear, in turn tool life, can be said to be affected by the process parameters such as cutting speed, feed, depth of cut and the treatment provided to the tool material. In the present investigation, it is intended to find out the effect of process parameters and thereafter process parameters alongwith cryogenic treatment on the wear resistance of T42 tool steel.

In recent decades various materials are subjected to subzero temperatures (below 00C). The subzero treatment in case of certain tool materials showed a significant increase in lives of the tools and components. An increase in the range of 92-817 % was found when tools made of these materials were treated at -1960C [3]. R.F. Barron in his research claimed improvement in wear resistance of tool steels by cryogenic treatment. The improvement in wear resistance was attributed to conversion of retained austenite into martensite till Mf temperature and there after to carbide refinement in the matrix. Before cryogenic treatment the microstructure showed relatively large carbides (20 µm) dispersed in matrix and after cryogenic treatment carbides of size as small as a 5 µm were found in the matrix [4]. Fanju Meng et. al. carried out research on Fe-12 Cr-Mo-V 1.4C tool steel. The samples were subjected to cryogenic treatment at -500C and - 1800C. The wear resistance improvement was found to be ranging between 110 to 600 %. This improvement in wear resistance is attributed to transformation of retained austenite into martensite and thereafter preferential precipitation of η carbides [5]. V. Leskovek et. al. in their research on AISI M2 HSS treated at -1960C, conclude that there is improvement in wear resistance due to deep cryogenic treatment [6]. In another research carried out by V. Leskovek et. al. the microstructure of M2 HSS was found to be modified by combined effect of vacuum treatment and cryogenic treatment in order to optimize the ratio between hardness and fracture toughness. It was also confirmed by using a 'Navy C' ring test that the dimensional changes of this type of steel can be controlled by this treatment [7]. Yun et. al. used M2 HSS for cryogenic treatment effect study. The samples were subjected to various combinations of heat treatment, tempering process, cryogenic treatment and there after tempering. It was found that there is an increase of 11.5 % in bending strength and 43% in toughness. Hot hardness of the material was also found to be improved. This increase in properties was attributed to transformation of retained austenite into martensite and thereafter precipitation of ultra-fine carbides [8]. J. Y. Huang et. al. treated M2 tool steel rods and found that the DCT not only facilitates the carbide formation, increase in carbide population, and volume fraction in the martensite matrix, but also makes carbide distribution more uniform. This caused more improvement in wear resistance [9]. P. Cohen et. al. used M1 and T15 HSS materials for studying effect of cryogenic treatment. They conducted the drilling tests on different materials. There was significant increase in life of cryogenically treated drills. In addition, turning tests were conducted using T15 turning inserts. Tool life of cryogenically treated T15 inserts was found to be significantly longer [10]. Cryogenic treated D3, T1 and M2 tool steels for different temperatures such as -800C, -1100C, -1400C and

-1800C for different soaking times were tested for flank wear test and sliding wear tests. The results showed improvement in wear resistance by SCT which further improved by DCT for all the three materials in different proportions [11]. A Molinari et. al. carried out research on AISI M2 HSS and AISI H13 hot work tool steels. The samples made of both the materials were subjected to different combinations of quenching, tempering and cryogenic treatment (-1960C). These samples were used for laboratory tests and field test. The properties of the material were improved, leading to improved life and reducing costs, almost by 50 % [12].

There are variations in the improvement in wear resistance of tool steels due to cryogenic treatment because of the different constituents in basic material, cryogenic temperatures and soaking time. In case of majority of tool steels improvement in wear resistance is found due to cryogenic treatment, which increases as cryogenic treatment temperature decreases.

Nomenclature

TC Cryogenic temperature (OC)

V Cutting speed (m/min)

f Feed rate (mm/rev)

d Depth of cut (mm)

TL Tool life (min)

w Flank wear of tool (mm)

2. EXPERIMENTATION

2.1 Conventionally Treated T42 Tools Experimentation

A European source steel T42 HSS pieces were taken for experimentation. The composition of the T42 material is shown in Table 1.

Table 1 Chemical Composition of T42 Tool Steel

C

Cr

Mo

W

V

Co

1.27%

4%

3.6%

9.5%

3.2%

10%

The objective of experiments was to establish an empirical mathematical model for T42 tool - AISI 1018 work combination to find out wear at a given cutting speed, feed, depth of cut using conventionally treated tool samples. It was decided to use Taguchi OA technique for carrying out experiments. Taguchi's OA method was selected because it incorporates readily available fractional factorial matrices orthogonal arrays to minimize the number of experiments [13]. Taguchi's L8 27 array was finalized to be used. Out of cutting speed, feed and depth of cut it is known that cutting speed has the highest effect on tool wear, hence four levels of cutting speed were decided to be used. Column no. 1, 2 and 3 were merged to have column no. 1 and cutting speed was allocated to this column. Feed and depth of cut were used at two levels and allocated to column number 3 and 4. Thus modified L8 array having one factor at four level and two factors at two levels was finalized for experimentation. The tool samples were then subjected to heat treatment. The heat treatment was carried out in a salt bath furnace. The heat treatment cycle is as shown in Table 2

Table 2 Heat Treatment Cycle

HARDENING

Steel

Preheat 1

0C

Preheat II

0C

Austenitization temperature 0C

Heating medium

Quenching

T42

500

850

1230

Salt Bath

Oil

Triple Tampering was done at 5600C for two hours each in salt bath.After heat treatment all samples were ground and tool geometry was provided to the tools. These tools were used for turning AISI 1018 work pieces as per the process parameters as shown in Table 3.

Table 3 Process parameters and their values at different levels

Process Parameters Symbol Level 1 Level 2 Level 3 Level 4

Cutting Speed (m/min) V 75 80 85 90

Feed (mm/rev) f 0.075 0.125 ---- ---

Depth of cut (mm) d 0.3 0.5 --- ----

A CNC lathe (ACE CNC Jobber-XL 2728) was used for experimentation. In order to avoid bias modified L 8 array experiments are repeated three times keeping all other factors constant. After turning flank wear of the tools is measured with help of Mitutoyo tool maker's microscope.

2.1.1. Analysis of Data:

The results obtained form the experiments and measurements of tool flank wear are shown in Table 4. Y1, Y2 and Y3 refers to the response (flank wear) in the first, second and third replications respectively. Table 4 also shows the arithmetic average values and S/N ratio (dB) for the tool wear data.

Table 5 gives the ANOVA and 'F' test values with percentage contribution of each factor. From this it is clearly evident that cutting speed, feed and depth of cut i. e all the three factors have significant effect on wear of the tool. For the selected parameters for experimentation the analysis shows that feed, depth of cut and cutting speed are in descending order. The S/N ratio (dB) for different factor levels is graphically shown in Fig 1.

Table 4 Experimental results and S/N ratio of tool wear, w

Expt no

V

f

d

Tool wear ,w mm

Average

Tool wear, w mm

S/N ratio

dB

Y1

Y2

Y3

1

75

0.075

0.3

0.225

0.215

0.230

0.223333

13.0175

2

75

0.125

0.5

0.450

0.430

0.450

0.443333

7.0634

3

80

0.075

0.5

0.330

0.315

0.325

0.323333

9.8054

4

80

0.125

0.3

0.380

0.400

0.385

0.388333

8.2138

5

85

0.125

0.3

0.435

0.425

0.415

0.425000

7.4306

6

85

0.075

0.5

0.335

0.350

0.350

0.345000

9.2418

7

90

0.125

0.5

0.605

0.590

0.580

0.591667

4.5571

8

90

0.075

0.3

0.290

0.295

0.280

0.288333

10.8001

Table 5 ANOVA and 'F' test for tool wear data

Source

SS

DOF

Variance

F ratio

Pure sum of squares

%

Contribution

A

6.101592

3

2.033864

123.5959

6.05222454

13.09408

B

30.41907

1

30.41907

1848.538

30.4026172

65.77653

C

9.667498

1

9.667498

587.4845

9.65104252

20.88018

Error

0.032911

2

0.016456

0.249216

Total

7

100

Fig 1 Average S/N Ratio by Factor Level for Tool Wear

A- Cutting speed (m/min), B- feed (mm/rev), C- depth of cut (mm)

By using non linear regression analysis, an empirical mathematical model for evaluating effect of process parameters on tool wear (response) of T42 HSS obtained is given as follows.

ln w = V ln1.018034 + f ln7950.991 + d ln3.54646 + ln0.020476 ----- (1)

R2 = 0.997522

2.2.2. Confirmation Experiment:

Both the response and S/N ratio can be used to find out the optimum condition, which is basically the optimum combination of treatment levels for the given response. Since the quality characteristic, wear w, is a smaller-the-better characteristic, the smallest response is the ideal level for a parameter. The S/N ratio, however, will always be highest at the optimum condition, since it is always desired that the signal to be much higher than the noise. Since not all treatment combinations have been run in the experiment, this requires a separate analysis, which considers all possible treatment combinations. From data given in Table 4 the optimum treatment combination is selected. The optimum combination is therefore A4, B1 and C1, i. e. cutting speed 75 m/min, feed 0.075mm/rev and depth of cut 0.3mm. This optimum condition has been used in the experimentation.

The first step in verifying the optimum combination is to use a predictive equation to predict a response value given the contributions of each factor at its level in the optimum combination. A simple yet effective equation generally used for this type of study is given by Fowlkes and Creveling [14]

y predicted = yexp+( yA − yexp) +( yB − yexp) + (yC − yexp) ------------ (2)

where ypredicted = the predicted response value (in this case wear, w) or S/N ratio; yexp = the overall mean response of the experimental runs (in this case, wear, w) or S/N ratio; and yA , yB , yC = the response or S/N ratio effects for factors A, B and C (in this case V, Æ’, & d) at a given level for each. Applying this formula to the data in Table 4 predicted response at the ideal condition 0.221543 mm and predicted S/N ratio is 13.08975.

Next, the robustness of this parameter optimization can be verified experimentally. This requires prediction and confirmation runs of both the optimum condition and one of the other experimental combinations. Each treatment combination is predicted, and then three experiments were conducted at these combinations and tool wear was measured using the same experimental setup. The results of these confirmation runs, including response are shown in Table 6, which can be used to interpret robustness of this experiment. The "non-optimum" condition was the treatment combination which yielded the highest response in the experimental runs. The difference between the two predictions and verifications is used to help in interpreting any uniform data shifts. As seen from Table 6, the error between predicted and confirmation run result is very low. Also Table 6 shows the tool wear calculated by using the empirical mathematical model developed as per equation no 1. The error between actual measured wear and calculated form the empirical mathematical model for optimum condition experiments is 3.53549 %, which indicates validity of the developed empirical mathematical model.

Table 6 Results of Confirmation Experiments

Confirmation expt. combination

Chosen parametric values

Experimental results wear (mm)

Predicted values

Wear as per developed model

V

f

d

Expt1

Expt2

Expt3

Average

Wear

(mm)

%

error

Wear

(mm)

%

error

Optimum

75

0.075

0.3

0.225

0.210

0.215

0.2166

0.2215

2.25

0.2243

3.535

Non optimum

90

0.125

0.5

0.595

0.580

0.600

0.5916

0.593

0.177

0.592

0.176

3.2 Cryogenically Treated Tools Experimentation

The objective of experiments is to establish an empirical mathematical model for T42 tool - AISI 1018 work combination to find out wear at a given cutting speed, feed, depth of cut and cryogenic treatment temperature.

Taguchi's L16 [215] array was finalized to be used. Cryogenic treatment temperature was decided to be used at four levels. Column number 1, 2 and 3 were merged to create column no. 1 and cryogenic treatment temperature was allocated to this column. Also out of cutting speed, feed and depth of cut, already it has been established that cutting speed has the highest effect on tool wear; hence four levels of cutting speed were decided to be used. Column no. 4, 8 and 12 were merged to have column no. 2 and cutting speed was allocated to this column. Feed and depth of cut were used at two levels and allocated column number 5 and 6. Thus modified L16 array having two factors at four levels and two factors at two levels was finalized for experimentation [13].

After conventional heat treatment samples were subjected to cryogenic treatment in a cryoprocessor using liquid nitrogen. The levels of cryogenic temperature selected were based on the data available in literature. -80 0C was the level selected as it is martensite finish temperature for T42 tool steel and also it is the level already used by many researchers in their research. Then -185 0C is the lowest cryogenic temperature level that the cryoprocessor used for cryogenic treatment reaches for and this cryo temperature level was also used by many researchers while carrying out their research. In between -80 0C and -185 0C one more level was required to be selected. It was selected as -140 0C as per the literature reviewed [11]. As per the results of pilot experiments the lowest wear was for -185 0C treated samples with a soaking period of 8 hours. Increase in soaking period above 8 hours resulted in more wear and hence the soaking time for all cryogenic temperatures was finalized as 8 hours. Thus the temperature levels selected were room temperature, -800C, -1400C, and -1850C. All the samples were soaked for eight hours at the temperature mentioned above. The samples were triple tempered at 1500C after cryogenic treatment. The entire cycle comprising of conventional heat treatment, tempering, cryogenic treatment and re tempering is shown in Fig. 2. After cryogenic treatments all samples were ground and tool geometry was provided to the tools. These tools were used to machine AISI 1018 work pieces as per the process parameters as shown in Table 7.

Fig. 2. Cryogenic Treatment Cycle

Table 7. Process Parameters and Their Values at Different Levels

Process Parameters Symbol Level 1 Level 2 Level 3 Level 4

Cryogenic Temp (0C) Tc Room Temp -80 -140 -185

Cutting Speed (m/min) V 75 80 85 90

Feed (mm/rev) f 0.075 0.125 -- --

Depth of cut (mm) d 0.3 0.5 -- --

The same machining set up used for conventional tool experimentation was used for cryo treated tool experimentation. In order to avoid bias modified L16 array experiments were repeated three times keeping all other factors constant. After machining flank wear of the tools was measured with help of the same Mitutoyo tool maker's microscope.

3.2.1. ANALYSIS OF DATA

The results obtained form the experiments and measurements of tool flank wear are shown in Table 8. Table 8 also shows the arithmetic average values and S/N ratio (dB) for the tool wear data. Table 9 gives the ANOVA and 'F' test values with percentage contribution of each factor. From this it is clearly evident that cryogenic temperature, cutting speed, feed and depth of cut i.e. all the four factors have significant effect on wear of the tool. For the selected parameters for experimentations, the analysis shows that feed, depth of cut, cutting speed and cryogenic temperature are in descending order. The S/N ratio (dB) for different factor levels is graphically shown in Fig. 3.

Table 8. Experimental Results and S/N Ratio of Tool Wear, w

Expt

No.

Column

No.

Actual setting values

Results

Tool wear, w (mm)

Avg.

Tool wear, w (mm)

S/N ratio (dB)

for tool wear

1

2

5

6

TC

V

f

d

Set I

Y1

Set II

Y2

Set III

Y3

1

1

1

1

1

25

75

0.075

0.3

0.240

0.220

0.235

0.232

12.69689

2

1

2

1

2

25

80

0.075

0.5

0.310

0.320

0.315

0.315

10.03306

3

1

3

2

1

25

85

0.125

0.3

0.445

0.430

0.425

0.433

7.261888

4

1

4

2

2

25

90

0.125

0.5

0.585

0.580

0.590

0.585

4.656671

5

2

1

2

1

-80

75

0.125

0.3

0.290

0.310

0.315

0.305

10.30856

6

2

2

2

2

-80

80

0.125

0.5

0.440

0.420

0.425

0.428

7.362653

7

2

3

1

1

-80

85

0.075

0.3

0.255

0.240

0.260

0.252

11.97854

8

2

4

1

2

-80

90

0.075

0.5

0.325

0.315

0.305

0.315

10.03087

9

3

1

2

2

-140

75

0.125

0.5

0.400

0.385

0.395

0.393

8.103693

10

3

2

2

1

-140

80

0.125

0.3

0.360

0.350

0.345

0.352

9.076010

11

3

3

1

2

-140

85

0.075

0.5

0.290

0.295

0.310

0.298

10.50244

12

3

4

1

1

-140

90

0.075

0.3

0.245

0.270

0.260

0.258

11.74953

13

4

1

1

2

-185

75

0.075

0.5

0.205

0.195

0.195

0.198

14.04963

14

4

2

1

1

-185

80

0.075

0.3

0.190

0.170

0.185

0.182

14.80500

15

4

3

2

2

-185

85

0.125

0.5

0.485

0.475

0.490

0.483

6.314342

16

4

4

2

1

-185

90

0.125

0.3

0.355

0.360

0.355

0.357

8.954560

Table 9. ANOVA and 'F' test for Tool Wear Data

Source

SS

D. o f.

Variance

F ratio

Pure SS

% Contribution

A

0.0509

3

3.74567

15.15201

10.49539

9.0526670

B

0.0715

3

5.32555

21.54298

15.23505

13.140800

C

0.3104

1

71.4345

288.9676

71.18733

61.401760

D

0.0784

1

15.5583

62.93659

15.31110

13.20640

Error

7

0.24721

-

-

3.1983680

Total

15

100

Fig. 3. Average S/N Ratio by Factor Level for Tool Wear

A- Cryogenic temperature TC (OC), B- Cutting speed (m/min), C- feed (mm/rev), D- depth of cut (mm)

By using nonlinear regression analysis, an empirical mathematical model for evaluating effect of process parameters and cryogenic temperature on tool wear (response) of T42 HSS obtained is given as

ln w = Tc ln 1.00114 + V ln 1.019603 + f ln 14841.24 + d ln 3.016076 + ln 0.01781 ------ (3)

R2 = 95.15%

3.2.2. Confirmation Experiment

As discussed in section 3.1.2 the optimum combination for these experimentations is A4, B1, C1, D1 i. e. Cryogenic temperature -185OC, cutting speed 75 m/min, feed 0.075mm/rev and depth of cut 0.3mm. This optimum condition has not been used in the experimentations. Applying the equation to the data in Table 8, a predicted response at the ideal condition is 0.167mm.

Next, the robustness of this parameter optimization was verified experimentally. Three Experiments were carried out at both the optimum condition and one of the other experimental combinations and tool wear was measured using the same experimental setup. The results of these confirmation runs, including response are shown in Table 10, which can be used to interpret robustness of this experiment. The "non-optimum" condition was the treatment combination, which yielded the highest response in the experimental runs. As seen from Table 10 the error between predicted and confirmation run results are very low. Also Table 10 shows the tool wear calculated by using the empirical mathematical model developed as per equation no.3. The error between actual measured wear and calculated form empirical mathematical model for both the optimum and non optimum condition combination is very less, which indicates validity of the developed empirical mathematical model.

Table 10. Results of Confirmation Experiments

Confirmation expt. combination

Chosen parametric values

Experimental results wear (mm)

Predicted values

Wear as per developed model

TC

V

f

d

Expt1

Expt2

Expt3

Average

Wear

(mm)

%

error

Wear

(mm)

%

error

Optimum

-185

75

0.075

0.3

0.175

0.165

0.175

0.1716

0.167

4.571

0.177

3.522

Non optimum

25

90

0.125

0.5

0.595

0.580

0.600

0.5916

0.593

0.177

0.592

0.176

RESULTS AND DISCUSSION

From the analysis it is clearly evident there is effect of cryogenic temperature on the wear of the tool. i. e. wear reduces as cryogenic treatment temperature reduces. In other words the wear resistance improves as the cryogenic temperature reduces. This ultimately improves the life of the tool. This improvement in wear resistance is basically due to conversion of retained austenite into martensite up to Mf temperature and thereafter due to precipitation of ultra fine carbides and refinement of carbide particles in the microstructure.

The microstructures of conventionally treated and -185 0C cryogenic treated tools taken by SEM are as shown in Fig. 4 (a) and (b) respectively. In Fig. 4 (a), presence of coarse MC and fine M2C carbides can be seen in conventional T42 HSS and the carbides have segregated in the matrix. The microstructure of the specimen subjected to cryogenic treatment at -185 0C clearly depict increase in carbide content, size reduction of carbides and uniform distribution of carbides in the matrix. Due to this the wear resistance of the T42 HSS got improved. This validates that earlier research on other types of tool steel is also applicable to T42 HSS material in context of improvement in wear resistance by application of cryogenic treatment.

Fig. 4(a) Microstructure of Non Cryo T42 Sample Fig. 4(b) Microstructure of Cryo Treated Sample at -185OC

CONCLUSION

T42 tool steel has been used for establishing an empirical mathematical model for evaluating tool wear due to turning parameters. Taguchi's modified L8 orthogonal array is used for deciding number of experiments. The model developed can be used to predict tool wear of the tool for different combinations of cutting speed, feed and depth of cut during turning operations. The coefficient of determination R2 for the developed model is 0.997522, which indicates a very good validity of the model developed.

The results obtained from experimentation are confirmed by performing optimum and one of the not optimum conditions. The experimental results, predicted results and results obtained from developed model are in good agreement.

There is improvement in wear resistance of T42 HSS due to cryogenic treatment.

A regression model for evaluating tool wear due to turning process parameters with cryo treated tools is evolved using non linear regression analysis. As the coefficient of determination R2 for the developed model is 0.951487, it indicates a very good validity of the model developed. The results obtained from confirmation experiments validated the developed model.

The wear resistance improvement in case of T42 HSS material due to cryogenic treatment temperatures as calculated from the developed empirical mathematical model upto -800C amounts to 11.00233 %, for -1400C wear improvement percentage goes to 16.87794 % and for treatment upto -1850C the improvement as high as 21.03457 % is obtained.

The improvement in wear resistance of T42 steel up to Mf temperature can be attributed to conversion of retained austenite into martensite and there after the improvement can be attributed to refinement of carbide size, uniform distribution of carbides and precipitation of ultra fine carbides.

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