Optimization of Controllable Turning Parameters for High Speed Dry Machining of Super Alloy: FEA and Experimentation

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ScienceDirect Materials Today: Proceedings 4 (2017) 2203–2212

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5th International Conference of Materials Processing and Characterization (ICMPC 2016)

Optimization of Controllable Turning Parameters for High Speed Dry Machining of Super Alloy: FEA and Experimentation B.Satyanarayanaa *,M. Dileep Reddyb, P Ruthvik Nitinc, a

Professor, VNR-VJIET, Bachupally, Hyderebad-500090, INDIA Research scholar, VNR-VJIET, Bachupally, Hyderebad-500090, INDIA

b,c

Abstract In dry machining, Interference Temperature plays a major role for Tool Wear and change in workpiece properties. Interface temperature and Tool wear are mainly affected by controllable turning parameters such as cutting speed, feed and depth of cut. The research aim of this paper is to minimize tool wear by optimizing controllable turning parameters during high speed dry turning of super alloy Inconel 718. Full FactorialDesign of Experiments is planed with 3 Levels for the purpose. A Finite Element Analysis model is created using Deform 3D software.The FEA outcomes are compared with Experimental results.ANOVAis carried out for the experimental results and mathematical models are developed for both the responses and are used for formulating a multi objective optimization.Multi-objective optimization is carried out using Genetic Algorithm.Satisfactory results are obtained through this research work and can be adopted in Industries. ©2017 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of Conference Committee Members of 5th International Conference of Materials Processing and Characterization (ICMPC 2016). Keywords:Turning; Dry Machining; Tool Wear; Interface Temperature; FEA; Deform 3D; ANOVA; Genetic Algorithm.

1. Introduction Super alloys are mainly developed to increase efficiency of gas turbines, aerospace and defence applications. Decreasing cost, increasing productivity and quality are main challenges facing by the manufacturers, so continuous monitoring and optimization techniques are required. In modern manufacturing, Dry turning is most emerging machining technique which increases productivity along with good quality. Dry turning increase its influence in machining industries due to high speeds and increase in M.R.R. Material property of workpiece depends on temperature, which results a major role to study the thermal analysis during machining. Experimental approach to study machining properties is costly and time taking, so an alternative approaches developed mathematical simulation using numerical method. One of the important numerical methods used is Finite Element Method.

* Corresponding author. Tel.: +91 9849527813; E-mail address:[email protected] 2214-7853©2017 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of Conference Committee Members of 5th International Conference of Materials Processing and Characterization (ICMPC 2016).

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Deform 3D is a simulating software most widely used for simulation, designed to analyze various machining and heat treatment processes. INCONEL 718 is a nickel based super alloy developed by H. L.Eiselstein of the International Nickel Company which is used mainly for moderately high temperature applications[1]. Composition, mechanical and physical properties are presented in Table 1. Due to its high strength and malleability it is widely used in high aggressive environments, where quality and strength plays major role.Now-a-day, dry turning increases its influence in manufacturing. Dry turning is mainly used to keep the material properties unchanged, increase production, and surface roughness [2]. The criteria for evaluating the temperature represent, today, one of the most important production problems is tool life.As the interface temperature increases tool wear increases, temperature increases due to shear and friction created during machining. During machining, temperatures are more in primary, secondary and tertiary regions as show in Fig.1

Fig. 1. Sources of heat generation

The major part of heat is converted into heat is primary shear zone; this will affect the mechanical properties of the workpiece and interface temperature between tool and workpiece. It causes cater wear and rake wear. Therefore, itneeds to optimize temperature.Omar Fergania investigated the thermal effects of a machining process on the subsurface grain size and hardness through a modelling approach and determines a limit line between the areas where mechanical and thermal loading are the most affecting the microstructure of workpiece [3].Dr.B. Satyanarayana demonstrated the influence of temperature (process, work piece and tool) while machining Nickel based super alloy Inconel 718 interms of cutting parameters cutting and determined that cutting speed has major influence on the temperature [4]. Researchers focus on finding best cutting parameters,experimentally it is too expensive and time consuming. With analytical methods, only simple process can be determined but complex cutting processes cannot. At this point numerical method is the only approach. Now a day’s, Finite element method is most widely used technique to reduce cost and time [5]. FEM is an approximate or nearly accurate approach made it to increase its influence in computational so it is almost used for all computer aided designs. FEM is mostly used for thermal, stress, pressure and wear analysis. For present work, turning model is developed using FEA software Deform 3D, in which temperature is measured at interface of single point cutting tool and workpiece. The workpiece geometry is modelled using Deform machining module, which includes previous tool pass based on depth of cut and tool nomenclature [6]. Usui’s wear model is used to determine flank wear; tool wear is dependent of contact pressure, relative velocity and absolute temperature. The constants in this model are determined by using experimentally measured and simulated values for same conditions [7]. Tool wear is measured experimentally and contact pressure, relative velocity, absolute temperature values are takenfrom simulation for same conditions. Based on these values A and B constant values are calculated Usui’s empirical formulae. Therefore, the constants obtained are A= 2.5140x10-8 and B= 964.5. 𝑑𝑤

𝐵

= 𝐴 ∙ 𝜎𝑛 ∙ 𝑉𝑟𝑒𝑙 ∙ 𝑒 −𝑇 (1) 𝑑𝑡 Corina Constantin proposed an overview of the approach of FEM analysis of the machining process considering 3D modelling and different commercial software’s are compared. The 3D FEM models were applied to turning, drilling and milling operations in certain cutting conditions in Deform for highlighting the most important aspects involved in setting the initial data for modelling, simulation and also for obtaining information after simulation [8].Pavel Kovac et al., created finite element model of cutting phenomenon during machining process using

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ANSYS. They simulated a model of turning process with steel as work piece and HM P25 as tool and found out heat flux with respect to time, deformation of the tool and cutting forces [9]. Deform 3D is a FEM software based process simulation system designed to analyze various forming and heat treatment processes used by metal forming and related industries. It is available in both Lagrangian (Transient) and arbitrary Lagrangian and the Eularian modelling [6].In a journal, 3-D finite element modelling of precision hard turning has been used to investigate the effects of cutting edge micro-geometry on tool forces, temperatures, stresses and tool wear in machining of AISI 1045 steel using uncoated carbide inserts with four distinct edge preparations and found highest stress and strain on workpiece occurred in the primary shear zone due to the highest deformation in this region, followed by the secondary shear zone. The maximum generated temperature was also found on secondary shearing zone[10].Pramod Kumar N et al., Thermal analysis was done to determine the temperature distribution over machining interface. Turning of mild steel rod using HSS is one among the major machining operations in manufacturing industry and tool wear results obtained would significantly contribute to the cutting parameters optimization during manufacturing [11]. 2. Experimental Setup Dry turning experiments are performed on ACE MIROMRIC make super Jobber 500 model CNC lathe as shown in Figure 2. In this study, Cutting speed, Depth of cut and Feed are taken as controllable parameters. Three variables are considered with three levels and so, 27 [L27 33 orthogonal array] experiments are designed and conducted based on full factorial design of experiments. Preliminary experiments are conducted to limit the controllable turning parameters. In this project, turning parameters and their levels considered are shown in Table 1. Table 3 shows the specifications of Thermograph camera and tool makers’ microscope [4]. Table 1.Cutting parameters with levels Process parameters

Level I

Level II

Level III

Cutting speed (m/min)

40

50

60

Feed (mm/rev)

0.1

0.15

0.2

Depth of cut (mm)

0.1

0.2

0.3

For the workpiece a cylindrical bar (length 30mm and diameter 50mm) of INCONEL 718 has been used as a workpiece. The composition and mechanical properties are given in the following tables. Chemical composition of Inconel 718 is given in the table 2.In Dry machining cutting tool influences the quality and the cost of the machining. Cutting tool is selected based on the literature survey that is TiAlN (PVD coated) of nomenclature CNMG 120408. Table 2. Composition of INCONEL 718

Ni

Cr

Nb+Ta

Mo

Ti

Al

Co

C

Mn

Si

Ph

Sl

B

Co

Fe

50 to 55

17 to 21

4.75 to 5.50

2.8 to 3.3

0.65 to 1.15

0.20 to 0.80

1 max

0.08 max

0.35 Ma x

0.35 max

0.015 max

0.015 max

0.006 max

0.30 max

Remai ning

2.1 Instruments: Tool wear and interference temperatures, which are important in machining, are measured using Tool Maker’s microscope and FLIR IR Thermograph. Flank wear is measured and recorded by using a MITUTOYO digital tool maker’s microscope of specifications eyepiece 15X objective 2X, total magnification of 30X.Tool wear and interference temperatures are measured (Figure 3and Figure4), and values are tabulated as shown in Table 4.

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Fig. 2. Super Jobber 500

Fig.3. Tool Makers Microscope

Fig. 4. FLIR ThermographCamera

Table 3. Specifications of FLIR Thermograph Background temperature Emissivity

29.6°C 0.6

Transmission

1.00

Average Temperature

37.1°C

Image Range

29.7°C to 649°C

Camera Model Camera serial number Camera Lens

Flir E60 Flir 49003862 FOL18

Camera Manufacturer

Flir Thermography

Lens description

Standard

Calibration Range

10.0°C to 650.0°C

3. Simulation In this project, Deform 3D Finite Element Analysis software is used for simulating turning on INCONEL 718 using PVD coated tool. In postprocessor, process parameters, workpiece, and tool inserts are modelled. The tool (rigid) with nomenclature CNMG120408 is modelled using CATIA v5 (as shown in figure 5) and imported into postprocessor and material tungsten carbide with coating TiAlN (2micron) and TiN (2micron)[12] are assigned using material library. In Process conditions, environmental temperature is considered as 30oC, shear friction coefficient 0.75[14], heat transfer coefficient 45N/sec/mm/C [6]. In tool holder library, DCLNR is used as tool holder [6]. Work piece is taken as plastic type with room temperature with constant length for all simulations [13]. Tool is meshed using relative mesh size of 15000 elements with size ratio 4 [6] and workpiece is meshed using relative mesh of 20000 elements with density mesh window with size ratio 1 as the meshing is important for chip forming as shown in Figure 6. From workpiece material library INCONEL 718 is assigned to workpiece. In simulation controls, number of steps is taken based on feed and length of the workpiece i.e. number of steps is length of workpiece per constant die displacement, arc length to cut is length of workpiece. Tool movement per step is taken as ¼ of the smallest element size i.e. 1/8 of the smallest element. Simulated using simulator and values are obtained using post processor [6].

Fig. 5. Cutting Tool (Modelled using CATIA V5 R16)

Fig. 6. Meshed Tool(left) and Work Piece(right)

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Based on the full factorial design of experiments total 27 experiments are conducted experimentally and simulated. Experimental setup is shown in figure 7 and simulation of first experiment is shown in the figure 8. Table No: 4 shows the results of flank wear and temperature of both experimental and simulated.

Fig. 7.Experimental setup

Fig. 8.Simulation

4. Results and Discussion: Table 4 shows the design of experiments with flank wear and interface temperature of both experimental and FEA results. Measuring of temperatures for first experiments experimentally and FEA are shown in figures 9 and 10. Table 4. Results for Flank wear and temperature

Expt No.

Speed (m/min)

Feed (mm/rev)

Doc (mm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

40 40 40 40 40 40 40 40 40 50 50 50 50 50 50 50 50 50 60 60 60 60 60 60 60 60 60

0.1 0.1 0.1 0.15 0.15 0.15 0.2 0.2 0.2 0.1 0.1 0.1 0.15 0.15 0.15 0.2 0.2 0.2 0.1 0.1 0.1 0.15 0.15 0.15 0.2 0.2 0.2

0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3 0.1 0.2 0.3

FLANK WEAR (mm) 0.067 0.083 0.098 0.065 0.085 0.098 0.053 0.071 0.081 0.076 0.093 0.103 0.081 0.098 0.103 0.069 0.085 0.094 0.095 0.107 0.114 0.093 0.108 0.112 0.084 0.092 0.101

SIMULATED FLANK WEAR(mm) 0.0652 0.0848 0.0921 0.09755 0.07914 0.06907 0.04416 0.05179 0.08811 0.07023 0.10257 0.11764 0.09285 0.10462 0.10024 0.06103 0.06848 0.08618 0.09650 0.09778 0.11055 0.10525 0.11643 0.11720 0.08080 0.10419 0.11392

TEMPERATURE(oc) 455.1 468.5 507.1 425.5 458.3 508.8 424.4 457.6 510.7 449.7 511.1 574.2 448.9 513.5 582.3 445.8 519.7 594.5 449.3 523.8 617.7 435.2 532.3 632.8 463.5 562.1 670.5

SIMULATED TEMPERATURE (oc) 459 488 480 425 408 467 403 445 547 383 554 563 455 526 571 474 503 602 449 514 629 418 571 604 441 529 653

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Fig. 9.Thermal Image of 8th experiment

Fig. 10.Simulate Temperature of 8th Experiment

Figure 11 and figure 12 show the comparison between experimental and simulated results. As shown in graphs simulated and experimental results are nearly equal. 650 550

TEMPERATURE

450 350

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

.

Fig.11. Temperature comparison of experimental and simulated 0.12 0.1 FLANK WEAR

0.08 0.06 0.04

1

3

5

7

9

11 13 15 17 19 21 23 25 27

Fig. 12.Flank Wear comparison of experimental and simulated

520 500 480 460 440 420

800 Temperature (oc)

Temperature (oc)

Variation of Temperature with Flank wears: Figures 13 shows influence of temperature on flank wears at constant speed, and varying speed. As shown in graph, as interface temperature increases flank wear increases due to increase in interface temperature the tip of the cutting tool leads to thermal softening, which results in reduce in hardness of the tool tip and increase of the tool wear. Figure 16 shows the contours of tool wear and temperature.

0.067

0.083 Flank wear (mm)

(a)

0.098

600 400 200 0

0.081

0.094 Flank wear (mm)

Fig. 13. Relation between Temp and Flank wear with a) Constant speed b) varying speed

(b)

0.101

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Temperature oc

Influence of depth of cut on Temperature:Figure 14 shows the relation between interface temperature and depth of cut (0.1, 0.2, and 0.3). As shown in graph, as depth of cut increases the interface temperature also increases due to high pressure at the interface between tool and work piece. From figure 13 the interface temperature increase Flank Wear increases. 700 650 600 550 500 450 400

1

2

3

4

DOC 0.1 MM

5

6

DOC 0.2MM

7

8

9

DOC 0.3 MM

Fig. 14. Relation between DOC and Temperature

Influence of Speed on temperature:Figure 15 shows the relation between interface temperature and speed. As shown in graph, as the speed increases the interface temperature also increase. This is due to as the cutting speeds increases the strain rate increases at shear zone thus more heat is generated rapidly with respective to speed results. Due to high speeds the sliding velocity also increasesinterface temperature due to as the speed increase the amount of heat dissipation decreases at interface temperature which results in increase of temperature.

Temperature oc

700 600 500 400

1

2

3

SPEED 40 M/MIN

4

5

SPEED 50 M/MIN

6

7

8

SPEED 60 M/MIN

Fig. 15. Relation between Speed and Temperature

Fig. 16. Contours of Tool wear and Temperature

9

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Table 5 & Table 6 shows ANOVA for flank wear & Interface temperature respectively. The value of “p” for both the models is less than 0.05 which indicates that the model is adequately significant at 95% confidence level, which is desirable as it indicates that the term in the model, have a significant effect on the response. The most significant factor for flank wear is cutting speed. The next contribution is on DOC and feed. In present work, R2 value is 0.9728 and the Adj. R2 is 0.988716. The predicted R2 value 0.970232 is in reasonable agreement with Adj. R2 value. The most significant factor for interface temperature is cutting speed. The next contribution is on depth of cut and Feed rate. In present work, R2 value is 0.9728 and the Adj. R2 is 0.988716. The predicted R2 value 0.970232 is in reasonable agreement with Adj. R2 value. The R2 value in this case is high and close to 1, which is desirable.Equation1 and Equation 2, for both the models, for flank wear andtemperature respectively were generated through Regression Analysis. Wear = ((-0.0666) + (0.4211*Doc) + (0.6722*Feed) + (0.0014*Speed) + (-0.1166*Doc*Feed) + (-0.0031*Doc*Speed) + (0.0006*Feed*Speed) + (-0.3166*(Doc^2)) + (-2.6666*(Feed^2)) + (0.0000016*(Speed^2)))

… (1)

Temp = ((535.0968) + (-1267.0111*Doc) + (-2283.2777*Feed) + (4.0852*Speed) + (1619.3333*Doc*Feed) + (29.2833*Doc*Speed) + (23.8833*Feed*Speed) + (568.1111*(Doc^2)) + (2892.4444*(Feed^2)) + (-0.0979*(Speed^2))) Source Model A-Speed B-Feed C-Depth of Cut AB AC BC A^2 B^2 C^2 Residual Cor Total

Table 5. ANOVA for Flank wear Sum of Mean F df Squares Square Value

… (2)

p-value Prob> F

0.006119 9 0.00068 165.5054 0.002713 1 0.002713 660.5386 0.000624 1 0.000624 151.9586 0.002335 1 0.002335 568.3572 4.08E-06 1 4.08E-06 0.994033 0.000114 1 0.000114 27.77208 1.33E-06 1 1.33E-06 0.324582 6.02E-05 1 6.02E-05 14.64678 0.000267 1 0.000267 64.91647 1.67E-07 1 1.67E-07 0.040573 6.98E-05 17 4.11E-06 0.006189 26 Table 6.ANOVA for Temperature

9.3E-15 4.8E-15 6.66E-10 1.67E-14 0.332739 6.25E-05 0.576319 0.001349 3.31E-07 0.842755

Source

Sum of Squares

df

Mean Square

F Value

p-value Prob> F

Model A-Speed

119524.2 80152.07

9 1

13280.47 80152.07

507.0172 3060.018

7.49E-19 1.23E-20

B-Feed C-Depth of Cut AB

472.6788 25028.3 786.6721

1 1 1

472.6788 25028.3 786.6721

18.04577 955.5219 30.0333

0.000542 2.22E-16 4.06E-05

AC BC

10290.16 1711.241

1 1

10290.16 1711.241

392.8543 65.33116

3.47E-13 3.17E-07

A^2 B^2 C^2

193.6501 313.7338 575.7175

1 1 1

193.6501 313.7338 575.7175

7.393108 11.97762 21.97954

0.014584 0.002987 0.000211

Residual Cor Total

445.2867 119969.5

17 26

26.19333

significant

Significant

These equations were used for generating a code for performing Multi Objective Genetic Algorithm (GA) for obtaining optimised parameters. GA was performed in MATLAB. The genetic Algorithm yields not just one optimized solution but many solutions known as decision variables. An optimized solution can occur at many settings, and since there is more than one response to be considered, the results are always in the form of decision variables. These optimized solutions are shown in the table 7. An engineer can select one setting from the following set of decision variables so as to obtain the corresponding response values. Some of the solutions were confirmed by performing confirmation experiments.

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Table 7.Optimised solutions generated through GA – Decision variables Speed (m/min)

Feed (mm/rev)

Doc (mm)

Flank Wear (mm)

Temperature (OC)

40

0.05

0.1

0.0505

486.82

40.028

0.1154

0.1

0.0667

441.92

40.055

0.1428

0.1001

0.0667

430.53

40.042

0.0682

0.1002

0.0574

471.86

40.005

0.0531

0.1002

0.0518

484.2

40.049

0.0809

0.1004

0.0611

462.64

40.017

0.1094

0.1001

0.0662

444.99

40.126

0.0906

0.1002

0.0634

456.17

40.015

0.1004

0.1

0.065

450.02

40

0.05

0.1

0.0505

486.82

40.116

0.0759

0.1002

0.0598

466.17

40.062

0.0647

0.1

0.0562

474.63

40.09

0.0795

0.1001

0.0607

463.61

40.132

0.0876

0.1002

0.0628

458.13

40.024

0.0937

0.1001

0.0639

454.06

40.028

0.1125

0.1001

0.0665

443.38

40.02

0.0859

0.1002

0.0623

459.13

40.038

0.1066

0.1001

0.0659

446.55

40.148

0.0709

0.1001

0.0584

469.93

40.075

0.0613

0.1002

0.0551

477.34

40.048

0.0607

0.1001

0.0548

477.83

40.055

0.1428

0.1001

0.0667

430.53

40.003

0.052

0.1001

0.0514

485.08

40.012

0.0594

0.1001

0.0543

478.87

40.089

0.0686

0.1003

0.0576

471.66

40.011

0.0631

0.1

0.0556

475.84

40.001

0.0556

0.1

0.0528

482.05

40.152

0.0777

0.1004

0.0604

465.01

40.015

0.0503

0.1

0.0507

486.58

40.019

0.0962

0.1001

0.0643

452.56

Under number of decision variables, the best optimised parameters were found to be 0.101 depth of cut, 0.142 mm/rev feed, 40.055mm/min speed yielding the tool wear and interface temperature output of 0.0667 mm and 430.53OC respectively. 5. Conclusion This paper presents finite element analysis during turning of super alloy INCONEL 718 and validated by experimental data. Effect of cutting parameters on interface temperature and tool wear has been validated using statistical tool design expert. The finite element analysis enables to predict interface temperature and tool wear at every step as meshing will be re-meshed for each step using tool DEFORM 3D. The following conclusions are drawn.

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• Comparison of FEA results with experimental results show that the both results are almost equal. • The results indicate that Deform 3D is efficient tool for finite element analysis of turning. • The relative error between the experimental and FEA interface temperature is up to 10% and tool wear is up to 17% but FEA can reduce cost, time and resources. • ANOVA analysis for ‘Lower the Better’ characteristics indicated that the Cutting speedhas major influence on the Flank Wear and Temperature and secondly theDepth of Cut. • As the speed increases, temperature increases which tend to increase tool wears. • The best Optimum cutting parameters are 0.1 depth of cut, 0.14 mm/rev feed, 40mm/min speed yielding the tool wear and interface temperature output of 0.0667 mm and 430.53OC respectively Acknowledgements This work was supported and funded by UGC, India under Minor Research Project Scheme, grantedduring the period 2014 to 2016. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15]. [16]. [17]. [18]. [19]. [20].

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