7742


FACTA UNIVERSITATIS 
Series:Mechanical Engineering 
https://doi.org/10.22190/FUME210728067G 

© 2020 by University of Niš, Serbia | Creative Commons License: CC BY-NC-ND 

Original scientific paper 

IDENTIFICATION OF CHATTER VIBRATIONS AND ACTIVE 

VIBRATION CONTROL BY USING THE SLIDING MODE 

CONTROLLER ON DRY TURNING OF TITANIUM ALLOY 

(TI6AL4V) 

Mehmet Ali Guvenc1, Hasan Huseyin Bilgic2, Selcuk Mistikoglu1 

1Iskenderun Technical University, Department of Mechanical Engineering, Hatay, Turkey 
2Necmettin Erbakan University, Department of Aeronautical Engineering, Konya, Turkey 

Abstract. In recent years, with the development of sensor technologies, communication 

platforms, cyber-physical systems, storage technologies, internet applications and 

controller infrastructures, the way has been opened to produce competitive products with 

high quality and low cost. In turning, which is one of the important processes of 

machining, chatter vibrations are among the biggest problems affecting product quality, 

productivity and cost. There are many techniques proposed to reduce chatter vibrations 

for which the exact cause cannot be determined. In this study, an active vibration control 

based on the Sliding Mode Control (SMC) has been implemented in order to reduce and 

eliminate chatter vibration, which is undesirable for the turning process. In this context, 

three-axis acceleration data were collected from the cutting tool during the turning of 

Ti6Al4V. Finite Impulse Response (FIR) filtering, Fast Fourier Transform (FFT) analysis 

and integral process were carried out in order to use the raw acceleration data collected 

over the system in control. The system is modeled mathematically and an active control 

block diagram is created. It is observed that chattering decreased significantly after the 

application of active vibration control. The surface quality formed by the amplitude of the 

graph obtained after active control has been compared and verified with the data 

obtained from the actual manufacturing result. 

Key Words: Chatter, Sliding Mode Controller, Active Vibration Control, Turning, 

Ti6Al4V 

1. INTRODUCTION 

In recent years, smart manufacturing applications have found a wide range of uses in 

machining [1–3]. Increasing the quality in the machining process is possible by reducing 

                                                           
Received July 28, 2021 / Accepted November 10, 2021 

Corresponding author: Selcuk Mistikoglu 

Department of Mechanical Engineering, Iskenderun Technical University, 31200 Hatay, Turkey 
E-mail: selcuk.mistikoglu@iste.edu.tr 



2 M. A. GUVENC, H. H.BILGIC, S.MISTIKOGLU 

and controlling the vibrations (chatter) that occur during production. Chatter vibrations in 

turning and milling, which are among the most widely used methods in machining, cause 

important problems and directly affect the cost and product quality [4–7]. 

Chatter vibrations occurring during machining may cause early completion of the life 

of the cutting tool, increase in surface roughness of the workpiece and damage to 

machine parts. Chattering is a classic problem that occurs frequently and reduces 

production efficiency. The operator often encounters it while its cause is not entirely 

clear. In a good quality turning process, it is desirable that the chip thickness should be 

standard at the beginning and end of production. However, chatter vibrations cause the 

thickness of the chip in turning to be variable. This negatively affects the roughness of 

the machined surface and causes noise during turning [8-10]. These chatter vibrations, 

which are constantly encountered in machining applications, should be avoided without 

sacrificing quality and efficiency [11]. For this reason, the identification of chatter 

vibration and chatter suppression has been a subject of interest to researchers working in 

the field of machining. To overcome these problems, various techniques have been 

developed by researchers to detect, reduce and control of chatter vibration. Techniques 

suggested in the literature include determining of the effects of machining parameters on 

product quality, prediction of machining parameters for the best product quality, 

optimization of machining parameters, tracking/monitoring of the machining process, 

identification/detection of chatter vibrations, real-time control of chatter during machining, 

and also providing suitable numerical tools for modeling those effects [12-15].  

Passive control of the chatter vibrations during turning is an easy, applicable and 

efficient approach with correct element selection. The basis of the reduction of chatter 

vibration by passive control is the spring and damping element design suitable for the 

system frequency and amplitude [4]. However, the trend towards active control 

applications with different methods for a higher product quality has increased. In active 

vibration control applications in turning, there are stages of measuring the vibrations 

occurring during processing accurately, filtering/analyzing the measured data and 

creating an external impact force that can damp down the chatter vibration [16-18].  

There are many studies in the literature on the determination, identification, prediction 

and control of chatter. Alternative techniques to reduce chatter have been studied by J. 

Munoa et al. They proposed progressive solutions to design and control approaches and 

examined the success of the existing methods used to improve the cutting process 

[4,16,19]. In the study in which passive control and active control applications and future 

expectations were discussed, it is argued that active vibration control promises hope for 

the future in reducing chattering [20]. 

The chatter vibration generated during turning has high frequency and low amplitude. 

Therefore, sensors, data acquisition devices and actuators used in active vibration control 

must have high speed and precision. In a study conducted to measure the vibration values 

that occur on the cutting tool and are constantly increasing, Jang and Tarng used piezo-

electric sensors and measured and compared the controlled and uncontrolled vibration 

frequencies occurring in the cutting tool. They observed that the controlled vibration 

frequency values are 90% less than the uncontrolled vibration frequency values [21]. 

Magnetorheological (MR) and Electro-rheological (ER) fluids are among the systems 

commonly used in active vibration control applications. Mei et al. performed MR-based 

active vibration control for the boring process and achieved great success [22]. In 

addition to these, piezoelectric patches and actuators have expanded the application area 



 Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller... 3 

thanks to their high operating frequencies and precision [23]. Harms et al. Designed and 

implemented a piezoelectric-based cutting tool for active control on the lathe [24]. 

Haifeng et al. implemented a piezo-based active vibration control to suppress chatter 

vibrations that occur during the turning process [25].  

As seen from the studies given above and in literature, there are many studies on the 

identification, examination and control of chatter vibrations occurring during turning. 

However, it has been seen that there is not sufficient study on the determination of chatter 

vibrations during the turning of the Ti6Al4V alloy and the active control of these 

vibrations. It has been observed that the active chatter suppression studies in turning are 

generally carried out with classical control applications. By this paper, it is aimed at 

identification of chatter vibrations and active control of chatter by using SMC which is 

known robust, stable and optimal for Ti6Al4V. 

In this study, an active vibration control application working with a Sliding Mode 

based has been implemented in order to reduce the chatter, which adversely affects 

product quality, tool life and productivity in turning, during production. In this context, 

three-axis acceleration data were collected on the tool during machining, the collected 

data is filtered, and the double integral is taken for the displacement data, and then chatter 

analysis was performed using the Fast Fourier Transform (FFT) method. Active force 

control is carried out with the help of the mathematical model created. 

2. EXPERIMENTAL STUDY 

In the experimental studies, a Quantum turning lathe and Ti6Al4V titanium alloy 

workpiece with dimensions of Ø28 mm x 250 mm are used. Experiments were carried 

out with the processing parameters determined for this alloy. The image of the 

experiment set is given in Fig. 1. The constant parameters used in the experiments are 

given in Table 1. 

Table 1 The constant parameters used in experimental studies 

Workpiece Material Ti6Al4V 

Cutting Tool Tungsten Carbide 

Depth of Cut, a (mm) 1 

Cutting Speed, V (m/min) 50 

Feed Rate, Vc(mm/rev) 0.3 

Spindle Speed (rpm) 1200 

Ti6Al4V (Titanium Grade 5) Ti alloy used as a workpiece has a wide range of uses 

such as military equipment, navy, protection, aerospace, medical technology, ultrasonic 

thanks to its low specific gravity and high mechanical properties [26–28]. Ti alloys are 

generally selected according to phase transformations and Ti6Al4V alloy is in the α/β 

group. This alloy is often seen in aircraft landing gear, gas turbines, and implant 

applications. It is possible to see that Ti6Al4V alloy is frequently produced by casting, 

forging and machining methods, as well as powder metallurgy [29,30]. The chemical 

composition and mechanical properties of Ti6Al4V alloy are given in Table 2 and Table 

3, respectively. 



4 M. A. GUVENC, H. H.BILGIC, S.MISTIKOGLU 

 

Fig. 1 Experimental setup for vibration measurement 

Table 2 The chemical composition of Ti6Al4V (%) 

Al V C Fe O N H Ti 

5,5-6,5 3,5-4,5 0,08 0,25 0,13 0,03 0,012 Rest  

Table 3Mechanical properties of Ti6Al4V 

Mechanical properties   

Density (g/cm3) 4,45 

Specific heat (J/(g x K)) 0,56 

Thermal conductivity (W/(m x K)) 7,1 

Elasticity modulus (KN/mm2)  114 

Thermal elongation (10-6/K) 8,9 

Tensile strength (MPa) 892 

Yield limit (MPa) 828 

Elongation (%) 10 

2.1 Theoretical background 

There are two types of vibration in machining: spontaneous and forced vibration. 

These vibrations cause deterioration of the surface quality. It also wears the cutting tool, 

shortening its life and damaging the components of the bench. Forced vibration is a 

vibration that occurs as a result of the mechanical movements of the machine. The 

spontaneous vibration, on the other hand, occurs separately from the machine and 

external factors due to chip removal. This vibration is also called chatter vibration 

[12,31]. 

Chatter vibrations, which are seen as spontaneous vibrations during turning, are 

caused by machining forces, chip cross-section and changes in tool geometry. Due to the 

complex nature of the chatter vibrations, the cutting forces that occur during turning 

activate one of the structural modes of the cutting tool and the workpiece system, causing 

Data Acquisition System

3 Axis 

Accelerometer

Workpiece



 Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller... 5 

a wavy surface on the workpiece. It is of great importance to recognize the system 

elements in order to identify and control the chatter vibrations. 

Within the scope of this study, system modeling is carried out for the active vibration 

control application. The vibrations in the direction of the plunge axis with the highest 

vibration are taken into account and the mathematical model is created accordingly. The 

mechanism of chatter vibration is shown in Fig. 2. In this mechanism, m, k, c are the tool 

parameters and refer to mass, spring constant and damping coefficient, respectively. 

 

Fig. 2 System modeling in turning 

Time dependent mathematical expression of sawdust is shown in Eq. (1).  In the 

equation, the displacement of the cutting point over time with concern to the position in 

the previous cycle is expressed as x(t)-x(t-), where hs(shown as h0(t) in Fig. 2) is the 

thickness of the uncut chip. In the equation, it is defined as =60 ∕ 𝑁, where N represents 
the rotation speed of the spindle in rev/m.  

 ( ) ( ) ( )
s

h t h x t x t      (1) 

The equation expressing the system behavior during turning is presented in Eq. (2). 

Here, Fc is the cutting force, b is the depth of cut, Kf is the specific cutting coefficient. 

Specific cutting coefficient varies depending on the physical and mechanical properties of 

the material. 

 
( ) ( ) ( ) ( ) ( ( ) ( ))

c f s
mx t cx t kx t F t K b h x t x t       

 
(2)

 

In active control applications, an external actuating force is required to keep the tool 

vibration at the desired level. As expressed in Fig. 2, the acting force has been designed 

to affect the tool. In the case that the actuator force is added to the system, the 

mathematical model will be as in Eq. (3). Fact represents the impact force generated by 

the actuator. 

 
( ) ( ) ( ) ( ( ) ( )) ( )

f s act
mx t cx t kx t K b h x t x t F t      

 
(3)

 

The process parameters used to ensure real situations are [25,32,33]: mass (m) = 2.5 

kg, stiffness (k) = 2x10 N/m, damping (c) = 1340 N.s/m and cutting coefficient (Kf) = 

2000 N/m2. Eq.(4) is obtained if the transfer function between force and displacement for 

the cutting tool is defined as (s). With the addition of Fact to the system for Fig. 2, which 



6 M. A. GUVENC, H. H.BILGIC, S.MISTIKOGLU 

is adapted for the closed-loop feedback control strategy, the general transfer function of 

our system is obtained as in Eq.(5) and the system transfer function in Eq.(6).  

 
( ) ( )( ( ) ( ))

c act
x s s F s F s 

 
(4)

 

 
( ) ( )( ( ( )( 1)) ( ))

s

f s act
x s s K b h x s e F s





   

 
(5)

 

 
2 2

( ) 1
( )

( )

X s C
G s

F s ms cs k s As B
  

   
 (6) 

2.2 Data acquisition 

Within the scope of this study, the acquisition of vibration data generated during 

surface turning of Ti6V4Al alloy workpiece is performed with Dewesoft/Sirius, which 

provides a flexible, modular, expandable and safe working environment (Fig. 3a). The 

most important features of SIRIUS are that it prevents errors frequently encountered 

during the measurement process, is simple to use and has user-friendly data processing 

software. 

a)       b)  

Fig. 3 a) Dewesoft Data acquisition device b) MEMS triaxle accelerometer 

MEMS type PCB/3713B1130G brand accelerometer has been used to collect 3-axis 

acceleration data on the machine tool (Fig. 3b). The accelerometer has a measurement 

range of ±30 g and is produced with a titanium coating to prevent damage to interference 

data and external effects. The accelerometer has a measurement frequency of up to 1 

kHz. 

During turning, cutting force and tool vibration occur in three axes: rotation axis 

(radial), feed axis and plunge (depth) axis. Reducing vibration in any direction increases 

turning reliability and product quality. However, in this study, the vibrations in the 

direction of the plunge axis with the highest vibration were taken into account and a 

mathematical model was created accordingly. When choosing the location of the sensor, 

a point where the vibration will occur the most and where active control can be applied is 

selected. For this reason, the acceleration sensor is placed on the cutting tool and in the 

area close to the cutting point. 

2.3 Data processing 

The collected raw data may be insufficient to reach the desired result or use in control 

applications. Data processing is an important step in recalculating and filtering of signals, 

revealing signal statistics and ultimately achieving the desired result. 



 Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller... 7 

2.3.1 Filtering of acceleration data 

It is of great importance to clean and analyze the signals in order to use the raw data 

collected from the system. Filtering, one of the most basic applications of signal 

processing studies can be used for different purposes such as decreasing or increasing 

certain frequencies, decreasing or increasing the signal amplitude, cleaning and analyzing 

unwanted signals. 

Filters can also be used to condition a time history signal by reducing unwanted 

frequency content. In active control applications, filtering processes are often used to 

eliminate a disturbance or model the response of a physical system. It is possible to see 

both IIR (Infinite Impulse Response) and FIR (Finite Impulse Response) applications in 

active control applications. The formulas for FIR and IIR are detailed in Eqs. (7) and (8). 

 
0

( ) ( ) ( )
N

k

y n a k x n k


   (7) 

 
0 0

( ) ( ) ( ) ( ) ( )
N P

k j

y n a k x n k b j y n j
 

      (8) 

In these equations, x(n) is the input series in time, y(n) is the time series at the output, n is 

the total number of data points of the input signal in the time domain, with the series 

called a(k) filter a, N and P are the numbers of terms in the filters, also referred to as rank 

and number of links, respectively. For example, FIR filter is implemented between k=0 

and k=N. 

2.3.2 Integral of acceleration data 

It is often impossible to use the raw data collected from the system directly. 

Especially, in order to use the acceleration data from the system in sensitive active 

control applications, the acceleration data must be converted into displacement data. 

Thus, more accurate results can be obtained. The most important method used to convert 

acceleration data into position data is integrating [33,34]. 

The triaxial acceleration data collected during machining are arranged in the 

DewesoftX software interface and converted into displacement data to be input in active 

control applications. Then, the displacement data were input to the control model from 

the same point as the point where data was collected under real conditions. 

2.3.3 Fast Fourier transform (FFT) 

The Fourier transform, developed by the French mathematician Jean-Baptiste Joseph 

Fourier, argues that any function can be written as the sum of sine and cosine functions, 

and the amplitude of the sine and cosine waves can be determined using the integral. The 

basic equation used for the Fourier transform is given in Eq. (9). In this equation; Sx(f) is 

the output of the Fourier transform in the frequency domain, x(t), the function due to time 

and the frequency in radians 2πf [35]. 

 
2

( ) ( )
j ft

x
S f x t e dt







   (9) 



8 M. A. GUVENC, H. H.BILGIC, S.MISTIKOGLU 

FFT can be used to determine the high-frequency values in any signal. These 

frequencies can cause unwanted noise or vibration. FFT analysis is also widely used in 

the monitoring and fault detection of the vibration state of machine elements in constant 

motion such as bearings and gears. FFT analyses, which have an important place in the 

space, aviation and automotive industry, are performed by using multiples of two (512, 

1024, 2048) samples in the time block [36]. 

The general equation for FFT is given in detail in Eqs. (10) and (11), and k row 

represents the frequency element, N number of samples, i square root (-1) value, x(i) 

sampled signal data, n represents the index of the sample waiting to be processed. 

 
 

1

0

0,1, 2,( ) , 1
N

jn

k

n

x e kn N






  
 

(10)
 

 
1

*(2 * * / )

0

[ ]*
N

i k n N

n

Xk x n e







   (11) 

Within the scope of this study, in order to obtain information about the vibration that 

occurs during the turning process, FFT analysis has been carried out and examined for 

the acceleration data collected. Thus, information was obtained about what the operating 

frequency of the system was during processing, what the natural frequency range was and 

what the sampling number should be in data collection. 

3. ACTIVE VIBRATION CONTROL 

3.1 Sliding mode control theory 

In recent years, Sliding Mode Control (SMC), which has been used in many areas 

with increasing acceleration and is among nonlinear control methods, is preferred 

especially for meeting high-speed control needs. Among the biggest advantages of SMC 

is the ability to change the dynamic behavior of the system, to reduce the degree of the 

system and to minimize disruptive effects. SMC is preferred as an effective control 

approach against systems that cannot be modeled dynamically, variable parameters and 

distorting effects [37]. 

In the sliding mode control consisting of sliding and reaching stages, equivalent and 

switching rules must be realized, respectively [38]. So many functions such as tanh, sgn 

etc. are used to overcome the chatter problem, which is one of the biggest disadvantages 

of the sliding mode control [39, 40]. The transfer function given in Eq.(6) is written as a 

differential equation, as in Eq. (12). 

 �̈�(t)=A�̇�+Bx - Cu (12) 

Tracking error e(t) to find control input u(t) in the sliding mode control can be defined 

as follows, 

 
( ) ( ) ( )e t r t x t 

  
(13)

 

In the Eq. (13), e(t) is the tracking error, r(t) is the command signal, and x(t) is the 

measured signal. Sliding surface function can be expressed as Eq. (14). 



 Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller... 9 

 

1

( ) ( )

n
d

s t e t
dt





 
  
   

(14)

 

Assuming n = 2, Eq. (15) is obtained. 

 
( ) ( ) ( )s t e t e t 

 
(15)

 

In this equation, is a positive constant and if this equation is derivative, Eq. (16) is 
obtained. 

 ( ) ( ) ( )s t e t e t   (16) 

Substituting r-x for e in this equation, Eq. (17) is obtained. 

 s e r Ax Bx Cu      
(17)

 

The control input u(t), which is the sum of equivalent control (ueq) and switching 

control (usw), is given in Eq. (17). 

 
( ) ( ) ( )

eq sw
u t u t u t 

 
(18)

 

Due to the nature of the sliding mode controller, �̇� = 0 is expected. If Eq. (18) is 
arranged for �̇� = 0, ueq can be expressed as Eq. (19). 

 
 

1
( ) ( ) ( ) ( )

eq
u e t r t Ax t Bx t

C
    

 

(19)

 

With ksw=0.5 and Ω= 0.1, Switching function, usw can be expressed as in Eq. 
(20). 

 
( ) tanh( ( ) )

sw sw
u t k s t 

 (20) 

Acceleration data received from the cutting tool was used as feedback information in 

the turning process. With the help of the acceleration data, the necessary reaction force 

for the actuator is calculated. The closed-loop used for control is shown in Fig. 4. 

 

Fig. 4 SMC system block scheme 



10 M. A. GUVENC, H. H.BILGIC, S.MISTIKOGLU 

4. RESULTS AND DISCUSSION 

Within the scope of the experiments, chatter vibrations generated on the tool holder 

were collected and evaluated for three different cutting axes with the help of a triaxial 

PCB brand industrial accelerometer and Dewesoft/Sirius data collection device. The 

accelerometer is rigidly fixed on the tool holder without using any damping element.  

For the active control application, a data set of 20 s was chosen from the data 

collected during turning. Selected data is filtered, integrated and FFT analysis is applied. 

The amplitude values of the collected data ranged between 110 m/s2 in the positive 

direction and -132 m/s2 in the negative direction, and the data collection rate was realized 

as 2000 (2000 samples/s) per second. In order to determine the operating frequencies of 

the system, the vibration data collected was first subjected to FFT and critical frequency 

values were determined. After FFT analysis, it is observed that 110 Hz is the critical 

frequency value. This value is in the range of chatter vibration seen in the literature and 

confirms the study by Munoa in 2016, where he also mentioned the range of chatter 

vibrations (20-200 Hz) [1]. The FFT analysis results are given in detail in Fig. 5. 

 

Fig. 5 FFT analysis results 

In order to use the raw data collected from the system, it is of great importance to 

analyze the signals and to clean the unwanted signals. IIR or FIR filters are generally 

used in active control applications [2]. FIR filtering method was used to clean the 

parasitic data contained in the acceleration data and to obtain a clean data set that can be 

used in active control. 

11 m/s2, 108 Hz

FFT

Acceleration Data

0 100 200 300 400 500

f 
(H

z
)

0
.0

0
5

.0
0

1
0

.0
1

4
.0

0

t 
(s

)

-1
3

2
.0

0
-5

0
.0

0
0

.0
0

5
0

.0
0

1
1

0
.0

0

03:20 03.25 03.30 03.40

A
c
c
e
le

ra
ti

o
n

(m
/s

2
)

A
c
c
e
le

ra
ti

o
n

(m
/s

2
)



 Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller... 11 

The triaxial acceleration data collected during turning are arranged in the DewesoftX 

software interface and converted into displacement data by taking its integral so that it 

can be input in active control applications. The 20-second data randomly selected from 

the raw data for the SMC application is given in Fig. 6. 

 

Fig. 6 Uncontrolled 20 seconds displacement data 

Then, the displacement data were input to our SMC model from the same point as the 

point where the data was collected under real conditions, and the control of the model 

during processing was provided for 20 seconds. The displacement graph obtained after 

active control is given in Fig. 7. 

 

Fig. 7 Displacement data after active SMC 

While the displacement data were between 300-500 µm before control, it was 

observed to be between 50-100 µm after the control. This significant difference will 

allow us to obtain a high-quality product during turning. According to the information 

obtained from previous studies, chatter vibrations occur with average displacement 

amplitude of 200-300 µm. In Fig. 8, there is a visual of the surface roughness after 

normal turning and chatter vibration [1,3–5].  



12 M. A. GUVENC, H. H.BILGIC, S.MISTIKOGLU 

 

Fig. 8 Surface roughness after turning for Ti6Al4V workpiece 

In Fig. 9, there is a visual where the data obtained as a result of the active control is 

compared with the chatter data received from the system. As can be deduced from the 

visual, there has been a serious improvement in the quality of the surface roughness due 

to the decrease in the vibration amplitudes that occur during turning as a result of the 

active SMC of the cutting tool. 

 

Fig. 9 Data from controlled and uncontrolled situations 

It has been determined that the actuator generates a force of up to 25 N in order to 

decrease the amplitude during active control. As a result of the literature studies, it was 

seen that an actuator force of 18 N is required for active control of AA6025 aluminum 

alloy during turning, and 45 N for turning of C45 quality steel [6,7]. It is advocated that 

the force required by the actuator in active control applications varies depending on the 

mechanical properties of the workpiece, the properties of the cutting tool and the turning 

parameters. The information obtained from the literature confirms the results we have 

obtained [41-45]. The actuator force graph is given in the Fig. 10 for active control 

application. 

Chatter RegionStandard Region



 Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller... 13 

 

Fig. 10 Reaction force of the actuator 

FFT analysis of displacement data is given for uncontrolled and controlled situations, 

respectively, in Figs. 11 and 12. According to the FFT results, there has been a significant 

decrease in the critical frequency amplitude. This situation has revealed that the 

amplitude and frequency of the vibration that occurs after the control are too small to 

affect the product quality and system elements negatively. 

 

Fig. 11 FFT results for displacement data without active control 

 

Fig. 12FFT results for displacement data after SMC 



14 M. A. GUVENC, H. H.BILGIC, S.MISTIKOGLU 

5. CONCLUSIONS 

In this study, it is aimed to define chatter vibrations that negatively affect product 

quality and productivity in turning and to reduce them with active control application. 

Within the scope of the study, acceleration data in three axes were collected from the 

cutting tool during dry turning of titanium alloy Ti6Al4V quality material. The collected 

data is processed and then made available in active control applications. According to the 

results obtained from the study: 

 It has been observed that the amplitude values of the acceleration data collected on 
the tool during the dry turning of Ti6Al4V alloy vary between 110 m/s2 in the 

positive direction and -132 m/s2 in the negative direction. 

 According to the FFT result of the acceleration data, it is observed that the critical 
frequency values were 110 Hz. 

 It is observed that the displacement values before the control were in the range of 
250-350 µm. 

 After the active SMC application, it has occurred that the displacement data 
decreased to the range of 50-100 µm. 

 It is concluded that the FIR digital filtering method is an important step in active 
control applications. 

 It is concluded that the chatter problem, which is one of the major problems of 
machining, can be solved with active vibration control applications, a correct data 

acquisition mechanism, controller, system recognition processes and correct 

actuator selection.  

 It is concluded that the SMC works with high accuracy for active vibration control 
in turning. 

 It can be said that SMC is promising with its high accuracy and robustness for 
future studies. 

In line with the information obtained within the scope of the study, it is predicted that 

piezo and MR-based actuators will be of great importance for active vibration control in 

turning. 

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