Experimental Study of Deflection and Surface Roughness in Thin Wall Machining of Aluminum Alloy

Available online at www.sciencedirect.com

ScienceDirect Materials Today: Proceedings 5 (2018) 3745–3754

www.materialstoday.com/proceedings

ICMPC 2017

Experimental Study of Deflection and Surface Roughness in Thin Wall Machining of Aluminum Alloy B. V. Ramanaiaha, B. Manikantaa, M. Ravi Sankarb*, M. Malhotrac, K. K.Gajranib a

Department of Mechanical Engineering,Rajiv Gandhi University of Knowledge Technologies, RK Valley-516329, AP, India b Department of Mechanical Engineering, Indian Institute of Technology,Guwahati, Guwahati-781039, Assam, India c Department of Mechanical Engineering, Satyug Darshan Institute of Engineering and Technology, Faridabad, Haryana-121002, Haryana, India

Abstract Thin walled structures and other monolithic components are used in aerospace as well as automobile industries. They have structural homogeneity and excellent strength to weight ratio. Ribs, stingers, spars and bulk heads are few other monolithic components. These monolithic components are also used as structural parts. More workpiece material is removed in end milling, which is possible athigh speeds. In thin wall machining, thickness of wall is reduced graduallyduring cutting, which makes ita very complicated process. At high speed, deflection of the machinedthin wall work piece is expected due to end mill forces, machining error and reduced machining accuracy. In this current study, the thin wall aluminium workpiece is fabricated using high end milling speeds and deflections as well as surface roughness of thin walls are investigated.The work piece is machined on conventional milling machine. Experiments are carried out to study the deflection and surface roughness of the thin walls of the work piece at varying feed rate, speed and depth of cut. Deflections and surface roughness of thin walls are measured by optical microscope and non-contacting surface profilometer. The effects of process parameters on the deflection and surface roughness of thin walls are analysed by ANOVA regression technique. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization. Keywords:End milling; thin wall machining; surface roughness; deflection;aluminum alloy

1. Introduction Thin wall aluminum alloy parts demand is increased with the growth of the aviation industry, reducing weight of the structural parts, automobiles components, electronics enclosures and housing firms. In most cases, aluminum thin wall are used for heat sink purpose because of its good thermal and electricalconductivity. End milling is most preferable operation to manufacture thin walls. * Corresponding author. E-mail address: [email protected] 2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization.

3746

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

End milling operation reduces the thickness of the walls during the cutting. Due to reduction of the thickness, walls have tendency to deflect. Thin wall deflection causes non-uniformity of workpiece surface finish. Feed rate, speed and depth of cut are the most influencing process parameters on deflection and surface roughness. Researchers have studied thin wall machining during milling operations mostly in terms of high aspect ratio walls. Only few have studied effect of process parameters on the wall deflection and surface roughness. Ning,et al., [1] used finite element method (FEM) to calculate the thin walled structures deformations during machining. Budak [2] develop the analytical model to avoid the chatter in high performance milling without scarifying the productivity. Le et al. [3] obtained high aspect ratio mold steel thin walls having 15 µm thickness and 0.8 mm deep. Tang and Liu [4] simulated and calculated part deformations by FEM as well as reciprocal theorem, respectively, under linear load on the thin walled plates. Shamsuddin et al. [5] worked on finding the best cutting path strategy for machining thin walled aluminum alloys. Seguy et al. [6] formulated a numerical model using stability lobe theory to investigate surface roughness and chatter instability for thin wall machining. Tool-spindle dynamic interaction was analysed using FEM by Mane et al. [7]. Davies et al. [8] studied the thin walled part vibrations during milling. Auto–bispectra, auto-spectra, power spectra, time series, phase portraits and autocorrelations were investigates. It has been found that nonlinearities and damping because intermittent cutting effects a lot on the workpiece dynamics. Benardos et al [9] used different methodologies to predict surface roughness variations. A novel approach was applied by Min et al. [10] to instantaneously identify cutting force and cutter run out during flat end milling. Tlusty et al. [11] analyzed aspects which are special to the milling operation such as special character of dynamics of structure, chip thickness, direction of the cutting forces etc. Axinte [12] did empirical modeling and experimental study corresponding to surface integrity. Smith et al. [13] investigated damping and measured stiffness for tool holder-spindle interfaces. Movahhedy et al. [14] have done FEM modeling of cutting tool and spindle to obtain frequency of the response system using Timoshenko beam theory. Chip thickness, tooth radius, cutting geometry, exit and entry angles of an end milling cutter were developed using mathematical models. Developed models were used to predict cutting forces characteristics during end mill [15]. Dynamic stability lobe diagram method was used for predicting machining distortion and chatter influence [16]. Tang et al. [17] investigated variation of residual stress profile with respect to tool flank wear during aluminum alloy milling. Thevenot et al. [18] aims to optimize cutting conditions and critically determine milling cases for which vibrations are not apparent during thin wall machining. However, several researchers worked in deformations of flexible parts by FEM simulations, high aspect ratio walls, force prediction models and best cutting path strategy for good surface roughness. This paper analyse the effect of process parameters on deflection and surface roughness of aluminum alloy machined thin wall. 2. Experimental Details 2.1. Material Aluminum alloy is used as work piece material and cemented carbide as cutting tool. The dimension of the work piece was 100×100×5mm3. Cutting tool used has a flat end 4 mm diameter cutter.3-axis vertical conventional milling machine having maximum spindle speed of 2000 rpm is used for experiments. 2.2. Plan of experiments Central composite rotatable design (CCRD) is a modified version of 2k factorial design. In factorial design of experiment, a factor is assigned to two levels, coded as -1 and +1 respectively. A composite design is obtained by adding some extra levels called axial or star levels. This is done to make the system rotatable. CCRD contains twice star points as there are factors (i.e. 2*k) in design. Out of the parameters combinations added to the 2k plan of the experiment, some are parameter combinations where all the parameters have 0-level values, which are known as central point. These are conducted to test the consistency of the machine tool in terms of output response. Total number of experiments can be calculated as: Total number of experiments = 2k+ Axial runs (2*k) + Central runs (2*k),

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

3747

Since in the present work the number of variables, k, have been taken as 3, the total experiments required to be conducted is 20, (i.e. 8+6+6). The design for various input parameters are as shown in table.1 Table 1. Coded and original values of input parameters as per central composite rotatable design Coded values

−1.687

−1.000

0.000

1.000

1.687

Speed (rpm) Feed rate (mm/min)

90 31.5

125 40

180 50

250 63

355 80

Depth of cut (mm)

0.1

0.15

0.2

0.25

0.3

2.3. Methods In order to perform the thin wall machining, experimental setup is developed on a vertical milling machine. The rigid concrete foundation is under laid and the machine tool is bolted on it with foundation bolts. Fig. 1 (a) (b) shows overview of the experiment setup. Aluminum work piece is machined with varying feed rate of 31.5−80 mm/min, speed of 90−355 rpm and depth of cut 0.1−3 mm. Thin walls of 5mm thickness and 3mm height are fabricated with all process parameters. The wall of the work piece is machined from both sides using an alternate approach. To reduce the thickness of the wall, cutting proceeds until the 4mm height of wall with 0.3mm radial depth of cut. Finishing cut proceeded with 0.1mm radial depth of cut to decrease the deflection of aluminum thin wall. Other walls of aluminum are machined by the same procedure with different cutting parameters. (a)

(b

Spindle Tool

Workpiece Fig. 1: (a) Overview of thin wall machining experimental setup and (b) tool and workpiece interaction zone

Thin wall

Spindle

Tool

Workpiece

(a)

(b)

Fig. 2: (a)Overview of thin wall machining region and (b) fabricated thin walls of aluminum alloy

3748

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

Fig. 2 (a) and (b) shows obtained thin wall machined workpiece of the aluminum alloy. Deflection is measured by using an optical microscope. Fig. 3 (a-d) shows 3-D model, 3-D profile of thin wall surface, surface micrograph of T-joint and surface micrograph respective to it. Fig. 3 (e)shows 2-D profile of thin wall surface which also gives surface roughness of thin wall after machining. (a)

(b)

Thin wall surface

(c)

(d) Thin wall surface

Fig. 3 : 3-D model of a thin wall, (b) 3-D profile of thin wall surface, (c) surface micrograph of T-joint of machined thin wall, (d) surface micrograph of machined thin wall and (e) 2-D surface profile of machined thin wall of the aluminum alloy

3. Results and Discussion Output response from the set of experiments are analysed by ANOVA to examine the outcome of various process parameters on deflection and surface roughness (Ra) of the thin wall surface. ANOVA is used for the regression analysis. Coefficient of determination (R2) obtained from the regression analysis for deflection and surface roughness’s are 97.95 % and 97.13 %, which shows that the regression model fits well and response variables such as deflection and surface roughness. The results of the variance analysis show the strong quadratic model between the response variables (deflection and surface roughness) and independent process parameters. Quadratic model obtained from ANOVA analysis in terms of actual parameters are shown as:

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

3749

Deflection =1.743−(0.009117F)−(1.131D)−(0.0087S)+(0.011FD)+(0.000052FS) –(0.00436DS)+(0.000043 F2)+(2.52D2)+(0.0000124S2)

(1)

Surface roughnes =0.057 +(0.024F)−(0.549D)+(0.00029S)+(0.0056FD)−(0.000015FS)+(0.0019DS)– (0.00011F2) –(1.408D2) –(0.0000027 S2) (2) Where, F represents feed rate, D is depth of cut and S is speed of the spindle. 1.10

Depth of Cut 0.10 mm Depth Of Cut 0.15 mm Depth Of Cut 0.20 mm Depth Of Cut 0.25 mm Depth Of Cut 0.30 mm

Deflection (mm)

1.00 0.90 0.80 0.70 0.60 0.50 30

35

40

45

50 55 60 65 Feed rate(mm/min)

70

75

80

Fig. 4: Effect of feed rate on thin wall deflection at different depth of cut

Fig. 4 shows variation of deflection with feed rate at different depth of cut.Deflection of the wall increases with increase in feed rate of the spindle. Thus, heat generated between the tool and workpiece increases causing more tool wear. Heat degrades the cutting tool. As a result of degraded tool, more forces are generated which results in deflection of the walls as shown in Fig. 5 (a) (b) and (c). Deflection = 0.52 º

(a)

Deflection = 0.85º

(b)

Deflection=0.88 º

(c)

Fig. 5: Surface micrograph of deflected thin wall machined at feed rates of (a) 31.5 mm/min, (b) 63 mm/min and (c) 80 mm/min

Fig. 6shows effect of cutting speed on the deflection of thin wall at different depth of cut. With increase in speed, initially deflection tends to decrease up to 180 rpm due to increase in generated heat, which causes increase in temperature. Higher temperature causes material softening, which results in lesser forces. Contact length between chip and tool is less due to less contact length less. Therefore, deflection of walls decreases as speed increases (Fig. 7 a-b). Further increase in speed causes increase in deflection of the thin walls. Due to higher material removal rate at higher speeds tool chatter will occur. Therefore, deflection increases shown in Fig. 7 (c).

3750

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

1.00

Depth Of Cut 0.10 mm Depth Of Cut 0.15 mm Depth Of Cut 0.20 mm Depth Of Cut 0.25 mm Depth Of Cut 0.30 mm

0.95 Deflection (mm)

0.90 0.85 0.80 0.75 0.70 0.65 0.60 0.55 80

110 140 170 200 230 260 290 320 350 380 Speed (rpm)

Fig. 6:Effect of speed on thin wall deflection at different depth of cut

Deflection = 0.64 º

Deflection = 0.53 º

Deflection = 0.79 º

(b)

(a)

(c)

Fig. 7: Surface micrograph of deflected thin wall machined surfaces at different speeds of (a) 180 rpm, (b) 250 rpm and (c) 355 rpm

Fig. 8 shows the effect of depth of cut on deflection of the thin walls at different feed rate. With increase in depth of cut, there will be an increase in the deflection of the walls, due to high tool chatter. With more tool chatter, tool requires more force to withstand rigidly. This, increases deflection. It is observed from the results that the effect of depth of cut on deflection is very less compared to the feed rate and speed. Fig. 9 (a) (b) and (c) shows surface micrographs of deflected thin wall machined surface at different depth of cuts. 1.00

Deflection (mm)

0.90 0.80 0.70 0.60 0.50

Feed rate 31.5 mm/min Feed rate 40.0 mm/min Feed rate 50.0 mm/min Feed rate 63.0 mm/min Feed rate 80.0 mm/min

0.40 0.30 0.20 0.09

0.12

0.15 0.18 0.21 0.24 Depth of cut (mm)

0.27

0.30

Fig. 8:Effect of depth of cut on thin wall deflection at different feed rate

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

Deflection = 0.6 º

Deflection = 0.76 º

Deflection = 0.74 º

(a)

3751

(b)

(c)

Fig. 9: Surface micrograph of deflected thin wall machined surfaces at different depth of cuts of (a) 0.1 mm, (b) 0.2 mm and (c) 0.3 mm

Fig. 10 shows the effect of feed rate on thin wall surface roughness at different depth of cut. As feed rate increases, tool moves over the workpiece rapidly resulting in deterioration of surface. Also as feed rate increases, heat generation also increases causing more tool wear. As a result, surface quality of the walls decreases. Therefore, thin walls machined with higher feed rates will generate poor quality surfaces of thin walls as shown in Fig. 11 (a) and (b). 1.0

Surface roughness (µm)

0.9 0.8 0.7 Depth of cut 0.10 mm Depth of cut 0.15 mm Depth of cut 0.20 mm Depth of cut 0.25 mm Depth of cut 0.30 mm

0.6 0.5 0.4 30

35

40

45

50 55 60 65 Feed rate (mm/min)

70

75

80

Fig. 10: Effect of feed rate on thin wall surface roughness at different depth of cut

(a)

(b)

Fig.11: Surface morphology of machined thin walls at different feed ratesof (a) 31.5 mm/min and (b) 80 mm/min

3752

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

Fig. 12 shows the effect of speed variation on surface roughness at different feed rate. With increase in speed, surface roughness of thin walls decreases.At higher speeds, more heat generated on cutting zone, which reduce the bult up edge formation.As a result, contact length between the cutting tool and chip reduces. Therefore, surface roughness decreases. Better surface finish of machined thin walls are obtained with higher speeds and lower feed rates.Fig. 13(a) and (b) shows 3-D surface profile of machinedthin walls at different speeds. Feed rate 31.5 mm/min Feed rate 40.0 mm/min Feed rate 50.0 mm/min Feed rate 63.0 mm/min Feed rate 80.0 mm/min

Surface roughness (µm)

1.25 1.05 0.85 0.65 0.45 0.25 80

110

140

170

200 230 260 Speed (rpm)

290

320

350

380

Fig. 12: Effect of speed on thin wall surface roughness at different feed rate

(a)

(b)

Ra = 0.833µm

Ra = 0.448µm

Fig.13:3-D surface profileof machined thin wall at speed of (a) 90 rpm and (b) 355 rpm

Fig. 14 shows the variation of thin wall surface roughness with depth of cut at different speed. With increase in depth of cut, surface roughness is reduced. It is also detected that, depth of cut has minimal effect on the thin wall surface roughness as compared to feed rate and speed. Fig. 15 (a), (b)shows 3-D surface profiles of machined thin wall surfaces at different depth of cut. It is observed from the experiment results that better surface finish are obtained using combinations of lower feed rates, higher speeds and depth of cut as per as chosen cutting parameter.

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

0.95

Surface roughness (µm)

0.85 0.75 0.65 0.55 0.45

Speed 090 rpm Speed 125 rpm Speed 180 rpm Speed 250 rpm Speed 355 rpm

0.35 0.25 0.15 0.09

0.12

0.15

0.18 0.21 0.24 Depth of cut (mm)

0.27

0.30

Fig. 14:: Effect of depth of cut onthe thin wall surface roughness at different speed

(a)

Ra = 0.865µm

(b)

Ra = 0.765µm

Fig.15:3-D surface profileof machined thin wall atdepth of cut of (a) 0.1 mm and (b) 0.25 mm

4. Conclusion

In the present work, end milling operation is used for thin wall machining of aluminium alloy. End milling operation is complicated and difficult to fabricate thin walls component. The effect of process parameters on the deflection and surface roughness of machined aluminium alloy thin wall. Higher forces during end milling limit quality of thin wall surface finish. Experiments described in this paper allowed choosing the correct machined parameter combination for producing good accuracy of thin walls. As feed rate changes from 31.5 to 80 mm/min, deflection increases from 0.54 mm to 0.99 mm and surface roughness rises from 0.43 µm to 0.91 µm at constant depth of cut of 0.3 mm. It is observed that low feed rate, high speed and low depth of cut are suitable for controlling deflection of walls. For better surface finish, low feed rate, higher speed and high depth of cuts are suitable for machining of aluminium alloy thin walls.

3753

3754

Ramanaiah et al./ Materials Today: Proceedings 5 (2018) 3745–3754

Acknowledgment The authors are thankful for the financial support provided by Board of Research in Nuclear Sciences (Project Number: ME/P/MRS/02), Department of Science and Technology (Technology Systems Development Programme (DST/TSG/AMT/2015/619)) and Defence Research Development & Development Laboratory (CARS Project). References [1] H. Ning, W. Zhigang, J. Chengyu, Z. Bing, Finite Elemet Method Analysis and Control Stratagem for Machining Deformation of Thin Walled Components, Journal of Materials Processing Technology, 139(1−3)(2003)332–336. [2] E. Budak. Analytical Model for High Performance Milling. Part I: Cutting Forces, Structural Deformations and Tolerance Intergrity,International Journal of Machine Tools and Manufacture,46(12−13) (2006) 1489–1499. [3] P. Li, D. Zdebski, H.H. Langen, A.M.Hoogstrate, J.A.J. Oosterling, R.H. Munnig Schmidt, D.M. Allen,Micro Milling of Thin Ribs with High Aspect Ratio,Journal of Micromechanics and Micro engineering, 20(11) (2010) 115013-1−115013-10. [4] T. Aijun, L. Zhanqiang, Deformation of Thin Plates due to Static End Milling Force.Journal of Materials Processing Technology 206 (1−3) (2008)345–351. [5] K.A. Shamsuddin, A.R. Ab-Kadir, M.Z. Osman, A Comparison of Milling Cutting Path Strategies for Thin-Walled Aluminum Alloys Fabrication,The International Journal of Engineering and Science,2(3) (2013)1−8. [6] S. Seguy, G. Dessein, L. Arnaud, Surface Roughness Variation of Thin Wall Milling,Related to Model Interactions, International Journal of Machine Tools and Manufacture, 48(3−4)(2008) 261−274. [7] I. Mane, V. Gagnol, B.C. Bouzgarrou, P. Ray, Stability-based Spindle Speed Control During Flexible WorkpieceHigh Speed Milling, International Journal of Machine Tools and Manufacture,48(2) (2008)184−194. [8] M.A. Davies, B. Balachandran, Impact Dynamics in Milling of Thin Walled Structures,Nonlinear Dynamics, 22(4)(2000) 375−392. [9] P.G. Benardos, G.C. Vosniakos, Predicting Surface Roughness in Machining, International Journal of Machine Tools and Manufacture, 43(8) (2003) 833−844. [10] W. Min, Z. Wei-hong, T. Gang, Q. Guo-hua, New Cutting Force Modelling Approach for Flat End Mill, Chinese Journal of Aeronautics,20(3) (2007) 282−288.. [11] J. Tlusty, F. Ismail, Special Aspects of Chatter in Milling, Transactions of ASME, Journal of Vibration, Acoustics, Stress, and Reliability in Design 105(1) (1983) 24–32. [12] D.A. Axinte, R.C. Dewes,Surface Integrity of Hot Work Tool Steel after High Speed Milling—Experimental Data and Empirical Models. Journal of MaterialsProcessingTechnology, 127(3) (2002)325–35. [13] S.Smith,P. Jacobs, J.Halley, The Effects of Draw Bar Force on Metal Removal Rate in Milling, Annals of the CIRP, 48 (1996) 293–296. [14] M. R. Movahhedy, P. Mosaddegh, Prediction of Chatter in High Speed Milling including Gyroscopic Effects, International Journal of Machine Tools and Manufacture , 46(9) (2006) 996–1001. [15] W. A. Kline, R.E. Devore. The Effect of Runout on Cutting Geometry and Forces in End Milling, International Journal of Machine Tool Design and Research, 23(2-3) (1983) 123−140. [16] Q. Song, Z. Liuand, A. Xing, Influence of Chatter on Machining Distortion for Thin-Walled Component Peripheral Milling. Advances in Mechanical Engineering,(2014) 329564-1–329564-7. [17] Z.T.Tang,Z.Q.Liu,Y.Z.Pan,Y.Wan,X.Ai,TheInfluence Of Tool Flank Wear On Residual Stresses Induced By Milling Aluminium Alloy, JournalofMaterialsProcessingTechnology, 209(9) (2009)4502–4508. [18] V. Thevenot, L. Arnoud, G. Dessien, G. Cazenava-Larroche, Influence of Material Removal on Dynamic Behavior of Thin Walled Structure in Peripheral Milling,Machining Science and Technology, 10(3) ( 2006)275−287.