30120140507012

1. INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 7, July (2014), pp. 125-133
© IAEME: www.iaeme.com/IJMET.asp
Journal Impact Factor (2014): 7.5377 (Calculated by GISI)
www.jifactor.com
125
IJMET
© I A E M E
PREDICTION OF VIBRATIONS, CUTTING FORCE OF SINGLE POINT
CUTTING TOOL BY USING ARTIFICIAL NEURAL NETWORK IN
TURNING
Prof. L. B. Raut1, Prof. Matin Amin Shaikh2
1, 2(Department of Mechanical Engineering, SVERI’s College of Engineering Pandharpur, India)
ABSTRACT
In this paper the objective of this work is to develop a model to simulate the vibrational
effects of rotating machine parts on the single point cutting tool and cutting force acting on single
point cutting tool in turning. In this paper experimental studies were performed on turning process
vibration is measured with the help of accelerometer along with a device called as Fast Fourier
Transformer (FFT) Analyzer and cutting force is measured with the help of Tool dynamometer. The
vibration of single point cutting tool is sensed by accelerometer located on the tool-post of lathe
machine. The accelerometer will send the sensed vibration to FFT Analyzer which can be convert the
sensed data by using accelerometer shown in PC such as frequency, Amplitude, displacement so
on and cutting force is sensed by strain gauges which are compacted in tool post. The sensed force
will send to dynamometer, it displays the cutting force. The obtained experimental data given to an
Artificial Neural Network (ANN) in Matlab, with the help of experimental data ANN is to be trained.
And by using ANN can predict the vibrations and cutting force by changing parameters of turning
such as spindle speed, feed depth of cut. This model of ANN can be predict vibrations of single
point cutting tool and cutting force acting on single point cutting tool to avoid the failure of cutting
tool.
Keywords: Vibrations, Cutting Force, Cutting Tool, Turning, ANN, Prediction.
1. INTRODUCTION
Much emphasis has been placed upon vibrations in machine tools during recent years because
many people have recognized that accuracy, surface finish and, last but not least, production costs
are considerably influenced by them. Today an arsenal of sophisticated instruments is available for
the investigation of machine tool vibration. Cutting tool have always vibrated and will continue to do

2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
so. We strive to measure these vibrations and keep them at or below a tolerable level. While higher
cutting speeds generally contribute to an improvement of the surface finish obtained, but they
increase the vibrations of machine often excite components of the machine tool at their natural
frequency. The exciting force is trying to cause vibration of cutting tool. If the vibrations are
increased then the failure of cutting tool occurred. The failure of cutting tool results wastage of time,
money etc... In metal cutting operation our goals to increasing productivity, reliability and quality of
work piece. So through prediction of vibration of cutting tool by using some developed artificial
neural network. In this work strive to predict vibrations and cutting force coming on cutting tool at
the time of turning and keep them at or below a particular level to avoid the cutting tool failure.
2. EXPERIMENTAL SET UP INSTRUMENTATION
126
There are many parameters which affect the vibrations cutting force of cutting tool. In this
experimental study, the structural parameters for the machine tool variables are constant for every
experiment and also all the experiments have been completed on the same machine tool.Similarly,
cutting tool parameters are constant because the cutting tool used has the same characteristics. Also
the cutting parameters have been reduced to three to simplify matters. Variable cutting conditions
have been selected such as listed in Table 1. In this study 31 different cutting conditions have been
considered. The whole work was done on Conventional lathe machine. The work piece material used
was EN-8 and the tool used was TiN coated carbide insert. En8 has a hardness of 35 HRC. It is
mainly used for engine shafts, studs, connecting rods, dynamo and motor shafts etc. the workpiece
material in tests was selected to represent the major group of workpiece materials used in the
industry. The specimen was cylindrical bar with 40mm diameter. After removing the surface
imperfections on the workpiece the 31 different cutting conditions applied and respective vibration
cutting force are measured with the help of accelerometer-FFT Analyzer and Tool dynamometer
respectively.
Fig. 1: Actual photograph of Test rig Top view of Test rig with FFT Analyzer Tool
dynamometer
This present paper presents an experimental study to investigate the effects of cutting
parameters like spindle speed, feed and depth of cut on surface finish on EN-8. In this work EN-8
material used as workpiece and Orthogonal machining process is used. So, only two forces acting on

3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
the cutting tool Axial feed force or thrust force (Fa) acting in horizontal plane parallel to the axis of
workpiece and Cutting force or tangential force (Ft) acting in vertical plane and tangent to the work
surface. And the resultant force or total force (R) acting on the cutting tool can be calculated by
using below formula. The orthogonal cutting method is as shown in Fig. 2.
127
Fig. 2: Orthogonal machining
The cutting conditions was carried out without coolant and totally 31 experiments were
performed according to full factorial design. The photograph of experimental test rig is shown in Fig.
1. The vibration cutting force parameter generally depend on the manufacturing conditions like
feed, depth of cut, cutting speed, machine tool and cutting tool rigidity etc. In this study three main
cutting parameters, feed, cutting speed and depth of cut was used. Three cutting parameters for each
factor were used because the considered variables are multi-level variables and their outcome effects
are not linear. Table no. 1 shows the full experimental data. The Vibration graph shown by the FFT
Analyzer at Spindle speed 30rpm, feed 0.85mm/revol, depth of cut 0.5mm and at spindle speed 72
rpm, feed 0.85mm/revol, depth of cut 0.8mm is shown in Fig. 3.
Fig. 3 Vibration graph at Spindle speed 30rpm, Feed 0.85mm/revol, Depth of cut 0.5mm and at
Spindle speed 72rpm, Feed 0.85mm/revol, Depth of cut 0.8mm

4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
128
Table no. 1: All data to design ANN
Sr.
No.
Spindle
Speed
(rpm)
Feed
(mm/revol.
)
Depth of
cut (mm)
Vibrations
(RMS)
Cutting
Force (kN)
1 30 0.85 0.1 295.6 44.9
2 30 0.85 0.25 303.8 48.5
3 30 0.85 0.5 306 60
4 30 0.85 0.6 315.1 62.2
5 30 0.85 0.75 322.9 64.9
6 30 0.85 1.2 400.6 71.7
7 47 0.85 0.25 523 40.5
8 47 0.85 0.5 538 55.5
9 47 0.85 0.75 543 72.9
10 72 0.85 0.25 571 39
11 72 0.85 0.4 608 49.8
12 72 0.85 0.5 787 56.5
13 72 0.85 0.6 828 61.5
14 72 0.85 0.75 941 72.1
15 72 0.85 0.8 974 74.7
16 110 0.85 0.25 706 28.6
17 110 0.85 0.5 820 41.2
18 110 0.85 0.75 880 49.4
19 196 0.85 0.25 546 19.2
20 196 0.85 0.4 844 25.5
21 196 0.85 0.5 889 28.6
22 196 0.85 0.7 1067 36.4
23 196 0.85 0.75 1099 38.3
24 310 0.85 0.25 614 13.4
25 310 0.85 0.5 981 27.9
26 310 0.85 0.75 1463 39.6
27 473 0.85 0.25 700 10.3
28 473 0.85 0.5 851 19.2
29 473 0.85 0.75 1001 25.6
30 733 0.85 0.25 819 11.7
31 733 0.85 0.5 1122 24.4
3. ARTIFICIAL NEURAL NETWORK
Artificial neural networks are information processing systems, and since their inception, they
have been used in several areas of engineering applications. ANNs have been trained to solve non-linear
and complex problems that are not modeled mathematically. ANNs eliminate the limitations of
the classical approaches by extracting the desired information using the input data. Applying ANN to

5. International Journal of Mechanical l Engineering and Technology (IJMET), ISSN 0976
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp.
– 6340(Print),
a system needs sufficient input and output data instead of a mathematical equation. Furthermore
can continuously retrain for new data during the operation, thus it can adapt to changes in the system.
Artificial Neural Networks are non-brain.
-linear mapping structures based on the function of the human
brain. They are powerful tools for modeling, especially especia
when the underlying data relationship is
unknown. ANNs can identify and learn correlated patterns between input data sets and corresponding
target values.
After training, ANNs can be used to predict the outcome of new independent input data.
ANNs imitate the learning process of the human brain and can process problems involving non
linear and complex data. In this work, artificial neural network model have been developed to predict
vibrations cutting force in the machining of EN8 material.
4. PROCEDURE FOR PREDICTION
The experiment data is divided in to test data set. Test data is used to check the
the ANN model created to fit the sample of 31;
train the network is shown in Table no. 2,
number of nodes in hidden layers is being taken 2. The Levenberg
found to be the best fit for application because it can reduce the MSE to a significantly small value
and can provide better accuracy of prediction. The transfer function, training function, learning
function and performance functions used in this study are tansig, trainlm, learngdm and MSE
respectively. So a network of 3 input nodes, 2 hidden nodes and 1 o
network is structured. So neural network model with feed forward back propagation algorithm and
Levenberg-Marqudt approximation algorithm was trained with data collected for the experiment. The
neural network has training window is shown i
depends on the trial and error process. The regression graph shown by the
below Fig.5.
Fig. 4: ANN training tool
125-133 © IAEME
129
te preferred ratio selected is 9:22. The training data to
as well as test data is shown in Table no. 3. Next the
Levenberg-Marquardt training algorithm was
nd output node is created, so 3
ndow in Fig.4. The effectiveness of ANN model is fully
modeled network is shown
Fig. 5: Regression graph
it
lly non-linear
behavior of
utput 3-2-1
.

6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
130
After training the network considering above explained all parameters, the network is test
with test data. The graph between the actual and predicted values has also been plotted and from the
graph it is clear that the actual and predicted results come to a very close value.
5. RESULT DISCUSSION
After training the network the results shows that the training data and the predicted training
data has come to a very close value. The graph shows the result of the training data of the actual
value with the predicted value. For training data testing data, actual value predicted value of
cutting force are compared shown in Fig.6 and testing data training data, actual value predicted
value of vibrations are compared shown in Fig.7. The test data has predicted values are shown in
Table no.4. and the errors obtained in this model because of weights required training the ANN
model these weights NNTOOL taken randomly, this is trail error method we can’t change the
weights.
Table no.2: Training data for ANN
Sr.
No.
Spindle
Speed
(rpm)
Feed
(mm/revol.)
Depth
of cut
(mm)
Vibrations
(RMS)
Cutting Force
(kN)
1 30 0.85 0.1 295.6 44.9
2 30 0.85 0.25 303.8 48.5
3 30 0.85 0.5 306 60
4 30 0.85 0.6 315.1 62.2
5 30 0.85 0.75 322.9 64.9
6 30 0.85 1.2 400.6 71.7
7 47 0.85 0.25 523 40.5
8 47 0.85 0.5 538 55.5
9 47 0.85 0.75 543 72.9
10 72 0.85 0.25 571 39
11 72 0.85 0.4 608 49.8
12 72 0.85 0.5 787 56.5
13 72 0.85 0.6 828 61.5
14 72 0.85 0.75 941 72.1
15 72 0.85 0.8 974 74.7
16 110 0.85 0.25 706 28.6
17 110 0.85 0.5 820 41.2
18 110 0.85 0.75 880 49.4
19 196 0.85 0.25 546 19.2
20 196 0.85 0.4 844 25.5
21 196 0.85 0.5 889 28.6
22 196 0.85 0.7 1067 36.4

7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
131
Table no. 3: Testing data for ANN
Sr.
No.
Spindle
Speed
(rpm)
Feed
(mm/revol.)
Depth
of cut
(mm)
Vibrations
(RMS)
Cutting Force
(kN)
1 196 0.85 0.75 1099 38.3
2 310 0.85 0.25 614 13.4
3 310 0.85 0.5 981 27.9
4 310 0.85 0.75 1463 39.6
5 473 0.85 0.25 700 10.3
6 473 0.85 0.5 851 19.2
7 473 0.85 0.75 1001 25.6
8 733 0.85 0.25 819 11.7
9 733 0.85 0.5 1122 24.4
The error is calculated using absolute percent error given by the relation,

10. Fig. 6: Comparison of Experimental Predicted Cutting force of Train data Test data
respectively

11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
132

12. Fig. 7: Comparison of Experimental Predicted Vibrations of Train data Test data
respectively
Table no. 4: Error between Experimental values Predicted values
Sr.
No.
Vibration (RMS) Cutting Force (kN)
Experimental Predicted % Error Experimental Predicted % Error
1 1099 1073 2.41 38.3 40.1 4.5
2 614 614 0 13.4 13.56 1.2
3 981 981 9 27.9 26.85 3.93
4 1463 1463 3.76 39.6 37.21 6.41
5 700 700 2.94 10.3 10.16 1.38
6 851 851 0 19.2 19.5 1.5
7 1001 1001 0 25.6 26.12 2
8 819 810 1.11 11.7 10.3 13.6
9 1122 1105 1.54 24.4 23.7 2.95
6. CONCLUSION
From the results it can be easily seen that the minimum error obtained for the predicted value
of test data. This study concludes that the model of ANN can be predict the vibrations cutting
force of single point cutting tool at any three parameters such as spindle speed, feed depth of cut.
And this predicted value is nearly equal to actual value of vibrations cutting force respectively. So
with the help of ANN model we can easily predict the vibrations cutting force of single point
cutting tool without any experiment. And effectiveness of ANN model can be improved by
modifying the number of layers and nodes in the hidden layers of the ANN network structure.

13. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 125-133 © IAEME
133
REFERENCES
[1] D. J. Kim, B. M. Kim has present “Application of neural network and FEM for metal forming
process”.Sep.-1999.
[2] W. S. Lin, B. Y. Lee, C. L. Wu has presented “Modeling the surface roughness and cutting
force for turning”.April-1999.
[3] Thomas M. Beauchamp Y, Youssef A. Y. Masounave J. “Effect of tool vibrations on surface
roughness during lathe dry turning process”.
[4] F. M. Longbottom and F. D. Lanbam “Cutting temperature measurement while machining”.
[5] Bahaa Ibraheem Kazem, Nihad F.H. Zangana, “A Neural Network based real time controller
for turning process”. ISSN 1995-6665, Sep.-2007.
[6] Alex Sohn, Lucas Lamonds and Ken Garrard, “Modeling of Vibration in single- point
diamond turning” 2006.
[7] Dennis H. Shreve, “Introduction to vibration technology” IRD Mechanalysis, Inc. Columbus,
Ohio 43229, November 1994.
[8] Ashvin P. Yajnik, “Vibration in machine tools”.
[9] L. Håkansson, I. Claesson and P.-O. H. Sturesson, “Adaptive Feedback Control of Machine-
Tool Vibration based on The Filtered-x LMS-algorithm”.
[10] A. Bhattacharyya, “Metal cutting theory practice”, New central book agency (P) ltd. 8/1
Chintamoni das lane, Kolkata 700 009 India.
[11] Amitabh Ghosh, Ashok Kumar Mallik, “Manufacturing Science” Affiliated East-West press
private limited New Delhi.
[12] G.R.Nagpal, “Machine tool engineering” Khanna publishers.
[13] Serope Kalpakjian, Steven R. Schmid, “Manufacturing Processes” PEARSON.
[14] Bhandari V.B, “Design of Machine Elements”, Tata McGraw-Hill Publishing Company
Limited New Delhi, 2004, pp234-237.
[15] Ghosh and Mallik, “Theory of Mechanism and Machines”, East-West Press Private Limited
New Delhi India pp. 290-293.
[16] Vinay Babu Gada, Janakinandan Nookala, Suresh Babu G, “The Impact of Cutting
Conditions on Cutting Forces and Chatter Length for Steel and Aluminum” April 2013.
[17] Tejinder pal singh, Jagtar singh, Jatinder madan and Gurmeet kaur, “Effects of Cutting Tool
Parameters on Surface Roughness”, International Journal of Mechanical Engineering
Technology (IJMET), Volume 1, Issue 1, 2010, pp. 182 - 189, ISSN Print: 0976 – 6340,
ISSN Online: 0976 – 6359.
[18] Ajeet Kumar Rai, Richa Dubey, Shalini Yadav and Vivek Sachan, “Turning Parameters
Optimization for Surface Roughness by Taguchi Method”, International Journal of
Mechanical Engineering Technology (IJMET), Volume 4, Issue 3, 2013, pp. 203 - 211,
ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
[19] A. Hemantha Kumar, Krishnaiah.G and V.Diwakar Reddy, “ANN Based Prediction of
Surface Roughness in Turning”, International Journal of Mechanical Engineering
Technology (IJMET), Volume 3, Issue 2, 2012, pp. 162 - 170, ISSN Print: 0976 – 6340,
ISSN Online: 0976 – 6359.
[20] Nitin Sharma, Shahzad Ahmad, Zahid A. Khan and Arshad Noor Siddiquee, “Optimization of
Cutting Parameters for Surface Roughness in Turning”, International Journal of Advanced
Research in Engineering Technology (IJARET), Volume 3, Issue 1, 2012, pp. 86 - 96,
ISSN Print: 0976-6480, ISSN Online: 0976-6499.