Investigation of tool chatter in turning operation on lathe

Submitted by:Aakash Gautam (111601)

Abhay Rai (111603)Aditya Kumar (111610)

Devanshu Yadav (111628)Vijay Pratap (111689)

Under the guidance: Dr. Bhagat Singh Assistant Professor(SG)

Submitted to: Dr. Arun Kumar PandeyAssistant Professor(SG)

• Objectives

• Introduction

• Review of literature

• Theoretical analysis

• Simulink Model

• Fourier Frequency Spectrum

• Conclusion

OVERVIEW

OBJECTIVES

• Study of various parameters resulting in tool chatter during

orthogonal turning operation.

• Theoretical Analysis using Spring-Mass model.

• Simulation of vibration signal of tool chatter in turning operation

using MATLAB Simulink.

• Effect of various parameters on Tool Chatter.

INTRODUCTION

• What is tool chatter?

Tool chatter is defined as the relative movement between the work

piece and the cutting tool.

It results from vibration

Tool bounces in and out of the work piece

The vibrations result in waves on the machined surface

This affects typical machining processes, such as turning,

milling and drilling etc. It results in chatter marks

Chatter marks are irregular surface flaws developed on

the work piece during turning operation on a lathe, due to

machining vibrations

Results in a poor surface finish, high-pitch noise and

accelerated tool wear

This in turn reduces machine tool life, reliability and

safety of the machining operation

LITERATURE SURVEY

(1) Chatter was first identified as a limitation of machining productivity by Taylor [1], who

carried out extensive studies on metal- cutting processes as early as in the1800s.

(2) Arnold [2] examined numerous influences to which a tool is subjected during cutting

analytically as well as experimentally for lathes and other machines.

(3) Chatter is caused by instability in the cutting processes, which was first understood by

Tobias and Fishwick [3].

(4) Tlusty and Polacek [4] presented a stability condition in which stability limits can be

calculated based upon the system dynamics for orthogonal cutting.

(5) Knight [5] presented experimental stability charts for turning with a simplified

machine–tool structure model for various cutting conditions.

(6) Shanker [6] proposed a general method for the analytical evaluation of the stability

limit in oblique turning of a slender workpiece, held between the centers.

(7) Nurulamin [7] studied the mechanism of instability of chip formation on micro section

metallographic specimens of chip roots, received by instantly stopping the cutting process at

different phases of the full cycle of instability as well as on micro-section metallographic

specimens of the chip.

(8) Rahman and Ito [8] presented a method to determine the onset of chatter by online

measurement of the horizontal deflection of the workpiece using eddy current type

displacement pick- ups.

(9) Lee et al. [9] showed that the ploughing force acts like an additional damper in the

system after applying the ploughing force model in numerical simulations.

(10) Chiou et al. [10] approximated this chatter model with a linear model with first order

Fourier transform.

(11) Dimla and Lister [11] have used tool-post dynamometer as a force sensor to measure all

three cutting force components to find the static and dynamic components of the cutting

force.

(12) Chiou et al. [12] used an AE sensor to detect chatter in the presence of tool wear.

(13) Clancy and Shin [13] presented a three-dimensional frequency domain chatter

stability prediction model for face turning by including tool wear in the model. The results

showed that the flank wear and the stability limit were directly proportional to each other.

(14) Mahdavinejad [14] predicted the stability of a turning operation by finite element

analysis using ANSYS software. The flexibility of the machine’s structure, workpiece and

tool has been considered in this FEA model.

(15) Budak and Ozlu [15] compared a SDoF and multi-dimensional stability models by

several simulations and chatter experiments.

(16) Altintas et al. [16] presented a linear model to predict chatter stability.

(17) Suzuki et al. [17] presented an SDoF and a 2DoF analytical model by defining

equivalent transfer function to understand the effects of the cross transfer function and the

cutting force ratio on chatter stability.

(18) Urbikain et al. [18] presented an algorithm to predict stability in straight turning of a

flexible workpiece by Chebyshev collocation method.

A mathematical model considering a Single Degree of Freedom (SDoF)

orthogonal turning process with a flexible tool and relatively rigid work piece is

considered as shown in Fig. 2. The model incorporates various forces acting on

the physical system like the inertia force, damping force, spring force and the

cutting force.

Fig. 2. SDoF orthogonal turning model

Spring-Mass Model

When this SDoF flexible tool is cutting a rigid work piece, the equation of

motion of the dynamic system can be modeled in the radial (feed) direction as:

fmx t cx t kx t F t

f fF t K b x t T x t

is the cutting coefficient in feed direction,

b is the chip width (width of cut),

T is the time delay between current time and previous time,

[x(t-T)-x(t)] is the dynamic chip thickness due to tool vibration.

fK

(1)

(2)

The tool parameters m, k and c are the mass, stiffness and damping co-efficient,

respectively,

Substituting Eq.(2) in Eq.(1) and dividing by m gives:

fK bc k kx t x t x t x t T x tm m k m

(3)

Applying Laplace transform and using relations,

2n

km

2 ncm

fK bk

and assuming,

2 2 22 1sTn n ns s e (4)

From Eq. (4), the transfer function of the system with a sharp tool can be

obtained by direct derivation from differential equation as:

2 21

2 n ns

s s

(5)

Substituting into Eq.(5),where is the chatter vibration frequency,

the real and imaginary parts of the transfer function are found as:

s j

2 2nGR

2 nHR

(Real part)

(Imaginary part)

2 22 2 22n nR where

FACTORS INFLUENCING TOOL CHATTER

Speed

Feed

Depth of cut

Cutting parameters

The cutting parameters affecting tool chatter are shown in Figure

below in turning are:

Speed

Feed

Depth of cut (DOC)

Speed: At slow speed (relative to the vibration frequency),

as speed increases, chatter gets more significant.

Feed: Does not greatly influence stability, but control

amplitude of vibration.

DOC: The primary cause and control of chatter.

SIMULINK Used to model, analyze and simulate dynamic systems using

block diagrams.

Fully integrated with MATLAB , easy and fast to learn and

flexible.

It has comprehensive block library which can be used to

simulate linear, non–linear or discrete systems – excellent

research tools.

C codes can be generated from Simulink models for

embedded applications and rapid prototyping of control

systems.

SIMULINK MODEL

Aforesaid simulink model is used to generate time domain

signal at different cutting parameters.

Some of the signal in time domain are presented.

Further Fast Fourier Transformation (FFT) is done on these

signal in order to extract the frequency features of the

respective signals:

2.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-10

-5

0

5

10

15A

mpl

itude

( m

)

-10

-5

0

5

10

15

Fig 3. Simulated vibration signal.(a) Case1: depth of cut: 1mm.

Fig. 4. Simulated vibration signal.(a) Case2: depth of cut: 2mm.

0.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-30

-20

-10

0

10

20

30

Am

plitu

de (

m)

-30

-20

-10

0

10

20

30

1.xls

c:\documents and settings\b.singh\desktop\tc4\amplitude\1.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-30

-20

-10

0

10

20

30

40

Am

plitu

de (

m)

-30

-20

-10

0

10

20

30

40

Fig. Simulated vibration signal.(a) Case3: depth of cut: 3mm

5.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-1

-0.5

0

0.5

1

1.5

Am

plitu

de (

m)

-1

-0.5

0

0.5

1

1.5

Fig. Simulated vibration signal.(a) Case 4: feed: 0.6mm/rev

3.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-3

-2

-1

0

1

2

3

4

Am

plitu

de (

m)

-3

-2

-1

0

1

2

3

4

Fig. Simulated vibration signal.(a) Case 5: feed: 0.8mm/rev

2.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-10

-5

0

5

10

15

Am

plitu

de (

m)

-10

-5

0

5

10

15

Fig. Simulated vibration signal.(a) Case 6: feed: 1.0 mm/rev

9.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-0.1

-0.05

0

0.05

0.1

0.15

Am

plitu

de (

m)

-0.1

-0.05

0

0.05

0.1

0.15

Fig. Simulated vibration signal.(a) Case 7: speed 1200 rpm

11.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Am

plitu

de (

m)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Fig. Simulated vibration signal.(a) Case 8: speed 1600 rpm

8.xls

0 0.2 0.4 0.6 0.8 1Time (s)

-0.75

-0.5

-0.25

0

0.25

0.5

0.75A

mpl

itude

( m

)

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

Fig. Simulated vibration signal.(a) Case 9: speed 2000 rpm

From these time domain spectrum it is quite evident that the depth of cut is the

most influential parameter. With the increase in depth of cut chatter increases.

Feed is the second important parameter governing chatter. With the increase in

feed chatter increases.

Speed is the third important parameter controlling chatter. With the increase in

feed chatter increases.

5.xls

Fourier Frequency Spectrum

30.202

31.772

46.346

51.292

0 100 200 300 400 500Frequency (Hz)

0

0.4

0.8

1.2

1.6

2

Am

plitu

de (

m)

0

0.4

0.8

1.2

1.6

2

2.xls

Fourier Frequency Spectrum

36.995

0 100 200 300 400 500Frequency (Hz)

0

0.5

1

1.5

2

2.5A

mpl

itude

( m

)

0

0.5

1

1.5

2

2.5

1.xls

Fourier Frequency Spectrum

251.91

0 100 200 300 400 500Frequency (Hz)

0

0.6

1.2

1.8

2.4

3A

mpl

itude

( m

)

0

0.6

1.2

1.8

2.4

3

From these frequency spectrum it is quite evident that the instantaneous

frequency is spread throughout.

Moreover, in these spectrum we are not able to interpret the time information

i.e. at which corresponding time the frequency peaks are not desirable.

So, it is imperative that in order to have better understanding and analysis of

the chatter in turning we must go for other alternative approach.

Conclusions

• In this study, a chatter identification method for turning process was

presented.

• Simulink model was developed to simulate tool chatter in noisy

environment.

• It was observed , depth of cut, feed and speed governs the phenomenon

of tool chatter.

• Depth of cut is the predominant governing factor.

References

[1] F. Taylor, On the art of cutting metals, Transactions of ASME 28 (1907).

[2] R.N. Arnold, The mechanism of tool vibration in the cutting of steel, Proceedings of the Institution

of Mechanical Engineers 154 (1946) 261–284.

[3] S.A. Tobias, W. Fishwick, The chatter of lathe tools under orthogonal cutting conditions,

Transactions of ASME 80 (1958) 1079–1088.

[4] J. Tlusty, M. Polacek, The stability of machine tools against self excited vibrations in machining,

in: Proceedings of the International Research in Production Engineering Conference, Pittsburgh, PA,

ASME, New York, 1963, pp. 465–474.

[5] W.A. Knight, Chatter in turning: some effects of tool geometry and cutting conditions, International

Journal of Machine Tool Design and Research 12 (1972) 201–220.

[6] A. Shanker, An analysis of chatter vibration while turning slender work- pieces between centres,

Annals of CIRP 25 (1976) 273–276.

[7] A.K.M. Nurulamin, Investigation of the mechanism of chatter formation during metal cutting

process, Mechanical Engineering Res Bulleting 6 (1983) 11–18.

[8] M. Rahman, Y. Ito, Stability analysis of chatter vibration in turning processes, Journal of Sound and

Vibration 102 (1985) 515–525.

[9] B. Lee, Y. Tarng, S. Ma, Modeling of the force in chatter vibration, International Journal of Machine

Tools and Manufacture 35 (1995) 951–962.

[10] Y.S. Chiou, E.S. Chung, S.Y. Liang, Analysis of tool wear effect on chatter stability in turning,

International Journal of Mechanical Sciences 37 (1995) 391–404.

[11] D.E. Dimla, P.M. Lister, On-line metal cutting tool condition monitoring: I: Force and vibration

analyses, International Journal of Machine Tools and Manufacture 40 (2000) 739–768.

[12] R.Y. Chiou, S.Y. Liang, Analysis of acoustic emission in chatter vibration with tool wear effect in

turning, International Journal of Machine Tools and Manufacture 40 (2000) 927–941.

[13] B.E. Clancy, Y.C. Shin, A comprehensive chatter prediction model for face turning operation

including tool wear effect, International Journal of Machine Tools and Manufacture 42 (2002) 1035–

1044.

[14] R. Mahdavinejad, Finite element analysis of machine and workpiece instability in turning,

International Journal of Machine Tools and Manufacture 45 (2005) 753–760.

[15] E. Budak, E. Ozlu, Analytical modeling of chatter stability in turning and boring operations: a

multidimensional approach, CIRP Annals—Manufacturing Technology 56 (2007) 401–404.

[16] Y. Altintas, M. Eynian, H. Onozuka, Identification of dynamic cutting force coefficients and

chatter stability, CIRP Annals—Manufacturing Technology 57 (2008) 371–374.

[17] N. Suzuki, K.N.E. Shamoto, K. Yoshino, Effect of cross transfer function on chatter stability in

plunge cutting, Journal of Advanced Mechanical Design, Systems, and Manufacturing 4 (2010) 883–

891.

[18] G. Urbikain, L. N. Lopez de Lacalle, F. J. Campa, A. Fernandez, A. Elias, Stability prediction in

straight turning of a flexible workpiece by collocation method, International Journal of Machine Tools

and Manufacture 54–55 (2012) 73–81.