A1102030105

IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 11, Issue 2 Ver. III (Mar. – Apr. 2016), PP 01-05
www.iosrjournals.org
DOI: 10.9790/1676-1102030105 www.iosrjournals.org 1 | Page
Machining Performance estimation Model Based on Vibrations
Umamaheswara Raju. R. Sa,b*
V. Ramachandra Raju b
., R. Ramesha
.
a
Department of Mechanical Engineering, MVGRCE, Chintalavalasa, Vzm, AP, India.
b
Department of Mechanical Engineering, JNTUK, Kakinada, EG-Dist, AP, India.
Abstract: Manufacturing industry poses several challenges in addressing tolerance for geometry, dimensional
accuracy and surface finish. Former two can be controlled by integrating CAD-CAM and machining, whereas
surface finishestimation need to be integrated and automated. A real time vibration based systems yet to be
developed to control and monitor open architectural machine tools to achieve the desired surface finish. In this
work an attempt has been made to develop a technology that could provide relation between overall vibration
andsurface finish. A curve fitting model is developed to estimate surface finish. The results are encouraging and
the estimation model is 99.9% accurate with very less percentile error. This model can use for estimation of
surface finish and develop control systems for open architectural machine tools.
Keywords: Vibrations, Surface finish, Curve fitting Model.
I. Introduction:
The need for quality components to meet the market demand has become essential in contemporary
manufacturing. The components are usually quantified for their dimensional accuracy and surface finish for
proper life of the components. Dimensional accuracy of the products can be measured in line with the
manufacturing as said by many researchers. Co-ordinate measuring machines (CMM) can be used in line to
measure the dimensional accuracy of the components manufactured. Surface finish even being prime factor
cannot be measured real time in line with the manufacturing systems. Due to the advantages of computers and
advanced computational tools developing adaptive system for open architectural machine toolsbecame easier.
Systems as such can really reduce the number of trial errors experiments in achieving the required performances
and in turn reducing the cost. Rapid Cam Approach is such a system developed by [1].
Conventional stylus probe instruments are used, human efforts and intervention is definite for
measurement. The surface finish will be measured manually a destructive way where the probe physically
moves on the component In order to overcome to this problem a vibration based evaluation technique for an
open architectural machine tool is suggested to estimate the surface finish. Vibration affects the surface finish
and in order to estimate the surface finish various elements of surface finish are selected Lin and Chang [1].
Surface finish depends on many parameters like rigidity of the machine tools used; tool geometry and its
condition, type of cutting fluid used, machining conditions used like speed, feed and depth of cut (DOC) and
even vibrations [2]. Vibration is the prime factor for determining the performance and tool wear in boring
operations due to its tool length. Theeffect of cutting parameters on component vibration and surface finish are
investigated [3].In processes surface finish estimation using vibration was done for turning of Ti-Al-V alloy.
Vibrations are measured in multiple directions and a regression model is developed for the estimation of surface
finish [4].Precision End Milling operation is performed and vibration signal is measured from the sensors and
processed through singular spectrum analysis. Mathematical model is developed [5].
Turning operation is performed using diamond, tool vibration signal and surface roughness is
measured. D-optimal technique is used to design the experimentation. Response surface methodology (RSM) is
developed to mapping the data of vibration and surface roughness [6].Optimization of turning operation cutting
parameters is done to achieve the desired surface roughness. Signal to noise ration and anova techniques are
used for model development [7].High speed milling operations are performed and an online surface finish
monitoring system is developed using artificial neural networks (ANN) [8].
Machining process is uncertain and complex; in order to estimate the performance of the machine tools
several researched are using optimization tools for estimation in this author reviews various machining
processes and computational tools used for estimation [9]. Ceramic tool is used for machining cast iron. Various
elements of machining performance like Surface roughness, tool wear and vibration is measured. Surface
roughness is at constant state even the progression of vibration and tool wear [10]. Turning operation is
performed, surface roughness and vibration are measured while machining. A mathematical model is developed
for mapping the roughness and vibration [11].
The main aim of this work is to develop an intelligent system that could estimate the machining
performance from the vibrations. The purpose of this work is to develop system that could automatically
estimate the surface finish online while the machining operation is performed. The vibration sensors connected

Machining Performanceestimation Model Based On Vibrations
to the machine table will be able to give the ready data to the open architectural machine tools. The data
obtained can be feed to the advanced computational tools for optimization and estimating the performance ahead
machining is done. Such systems reduces the off line destructive type of measurement of surface finish, reduce
cost, time and human efforts. In open architectural machine tools intelligent system can even monitor the
vibration signal from the sensors and auto compensate the cutting parameters in order to achieve the required
performance [12 and 13].
II. Experimentation:
Milling operation is performed on aluminium on CNC Max-mill, cutter being Cloro-Mill R-245 from
sandvick. The sandvick coated carbide insetsR245-12-T3-H10 are used for machining aluminium. The range of
cutting parameters used for machining are selected from the sandvick catalogue as shown in the table 2.1. Five
different speeds (S), feed rates (F) and depth of cut (DOC) are selected. Design expert D-optimal technique is
used for generation of the machining data and totally 74 different experiments are performed.
Table 2.1 various cutting condition selected.
Sl. Speed
(rpm)
Feed
(mm/min)
DOC
(mm)
1 1560 470 0.1
2 1623 600 0.2
3 1688 750 0.3
4 1750 800 0.4
5 1815 1000 0.5
III. Surface Roughness Measurement:
A standard stylus probe instrument called Taly-surf is used for the measurement of the surface
roughness. The stylus is held manually on the work piece and probe is made in contact with the surface of the
work. The sampling length selected is 10mm and totally at three places on the work piece is measured for each
and every workpiece in order to have a better average value of the surface roughness (SR). The surface
roughness values for various cutting condition are shown in the table 4.1
IV. Vibration:
The overall vibrations (OV)of a face milling operation are measured using a FFT ANALYZER (DC-
12MTester). The accelerometer is placed on the work table as shown in the Figure4.1.This tester combine’s
advanced technology with high precision and value for efficient measurement of vibrations in the work table.
This probe is placed at work bench so as to gather the best possible information about the behavior of the work
bench while face milling. The vibration OV values for various cutting conditions are shown in the table 4.1
Figure 4.1 the vibration accelerometer placed on work table.
Table 4.1
Sl. S
(rpm)
F
(mm/min)
DOC
(mm)
OV
(m/s2)
SR
(Microns)
Predicted
SR
%
Error
1 1623 750 0.1 0.03769 0.45 0.435 0.033
2 1688 870 0.1 0.03958 1.2 0.9 0.25
3 1560 870 0.1 0.04563 0.25 0.236 0.056
4 1750 1000 0.3 0.04973 0.3 0.286 0.047
5 1560 870 0.5 0.05559 0.6 0.6 0
6 1623 470 0.3 0.5498 0.45 0.35 0.222
7 1623 600 0.1 0.06703 0.4 0.4 0
8 1688 470 0.3 0.07898 0.35 0.35 0
9 1750 750 0.5 0.07992 0.4 0.4 0
10 1815 600 0.3 0.08026 0.2 0.199 0.006

11 1623 750 0.3 0.09876 0.35 0.35 0
12 1688 1000 0.3 0.908 0.4 0.4 0
13 1815 750 0.1 0.1127 0.4 0.389 0.028
14 1750 870 0.5 0.134 0.5 0.5 0
15 1560 600 0.1 0.1353 0.3 0.289 0.037
16 1815 750 0.3 0.1375 0.25 0.25 0
17 1688 600 0.3 0.1383 0.3 0.285 0.05
18 1560 470 0.1 0.1456 0.3 0.3 0
19 1560 1000 0.1 0.2104 0.3 0.44 0.467
20 1750 600 0.1 0.6789 0.65 0.65 0
21 1560 750 0.3 0.2305 0.58 0.6 0.034
22 1688 1000 0.5 0.234 0.7 0.6 0.143
23 1560 870 0.3 0.2546 0.5 0.3 0.4
24 1688 470 0.1 0.2357 0.3 0.4 0.333
25 1750 470 0.1 0.2641 0.4 0.4 0
26 1750 1000 0.5 0.6832 0.5 0.317 0.367
27 1560 1000 0.3 0.2952 0.7 0.7 0
28 1560 750 0.1 0.3138 0.4 0.397 0.008
29 1623 750 0.2 0.3512 0.5 0.498 0.004
30 1688 870 0.1 0.3654 0.5 0.5 0
31 1560 870 0.1 0.3865 0.3 0.3 0
32 1750 1000 0.3 0.76898 0.4 0.45 0.125
33 1560 870 0.5 0.4109 0.2 0.2 0
34 1623 470 0.3 0.456 0.5 0.498 0.004
35 1623 600 0.1 0.458 0.6 0.6 0
36 1688 470 0.3 0.4946 0.3 0.289 0.037
37 1750 750 0.5 0.5064 0.4 0.4 0
38 1815 600 0.3 0.5226 0.4 0.367 0.083
39 1623 750 0.3 0.4965 0.35 0.35 0
40 1688 1000 0.3 0.5498 0.3 0.35 0.167
41 1815 990 0.1 0.5768 0.7 0.7 0
42 1750 1000 0.5 0.6034 0.6 0.6 0
43 1560 601 0.1 0.6089 0.5 0.5 0
44 1815 750 0.3 0.6547 0.5 0.3 0.4
45 1689 600 0.3 1.019 0.5 0.5 0
46 1560 470 0.1 0.6852 0.25 0.317 0.267
47 1560 1000 0.1 0.6872 0.2 0.321 0.607
48 1750 600 0.1 0.5498 0.3 0.35 0.167
49 1560 750 0.3 0.65467 0.3 0.3 0
50 1688 1000 0.5 0.76898 0.5 0.45 0.1
51 1560 870 0.4 0.8425 0.4 0.4 0
52 1688 470 0.1 0.8562 0.6 0.6 0
53 1750 470 0.1 0.8569 0.5 0.5 0
54 1750 1000 0.5 1.786 0.6 0.6 0
55 1560 1000 0.3 1.386 0.4 0.4 0
56 1560 750 0.1 0.918 0.3 0.276 0.08
57 1623 750 0.3 0.8675 1.2 1.2 0
58 1688 870 0.1 1.004 0.5 0.5 0
59 1560 870 0.1 0.9568 0.3 0.278 0.073
60 1750 1000 0.3 1.634 0.5 0.5 0
61 1560 870 0.5 1.041 0.25 0.25 0
62 1623 470 0.3 1.059 0.7 0.68 0.003
63 1623 600 0.1 1.127 0.4 0.4 0
64 1688 470 0.3 0.986 0.6 0.6 0
65 1750 750 0.5 1.219 0.7 0.68 0.029
66 1815 600 0.3 1.763 0.4 0.378 0.055
67 1623 750 0.3 1.367 0.5 0.5 0
68 1688 1000 0.3 1.453 0.5 0.5 0
69 1815 1230 0.1 2.078 0.35 0.359 0.026
70 1750 1350 0.5 1.54 0.45 0.45 0
71 1560 602 0.1 1.345 0.4 0.4 0
72 1815 750 0.3 1.634 0.5 0.498 0.004
73 1690 600 0.3 1.98 0.3 0.3 0
74 1560 470 0.1 1.754 0.4 0.4 0
V. Curve Fitting:
The data obtained in machining is unique and it’s difficult to establish a correlation in manufacturing as
it depends on several other parameters as said by many researches. In order to overcome such problems in
manufacturing, advanced models are developed in order to establish a proper relationship. In this work the

Curve Fitting in MAT-Lab is basically used to establish anaffiliation. Curve fitting interpolates the uneven data
into a plane and fits a smooth curve to these data points. This method is a unique the resultant curve passes
through all the data points and appears to be natural and smooth. It’s a polynomial function of third order
piecewise at succeeding disruptions of the data given locally the slope is determined. Curve looks to be similar
as that of the manually drawn curve and mathematically developed.
Based on the curve obtained for surface finish vs. Vibrations in MAT-LAB there exists a relationship
between surface finish and vibrations. The curve that fits to both the output parameters is smoothing spline.The
function that satisfies the fit is as follows and shown in equation (1).
F(x) =piece wise continuous computed at p. (1)
Where p varies between 0and1.
Based on function obtained incurve fitting module the predicted values of surface finish (f(x)) have been
calculated using analysis technique in curve fitting module as shown in figure 5.1. Curve fitting model could
predict the surface finish with average percentage of error of 0.06deviations as shown in table 4.1 and the model
accuracy of 99.9%.
Figure 5.1 MAT-LAB Curve fitting model.
VI. Result:
In this work the objective is to develop an intelligent system that could predict the machining
performances using machine table vibration signal. The system of this type being rare was the motivation factor
to this work. In this work face milling operation is performed on aluminium work piece. Five different cutting
conditions like S, F and DOC are selected, design of experiments are conducted using design expert and totally
74 different experiments are performed. Surface roughness and overall vibration signal is measured for each
experiment. Curve fitting model is developed in MAT-LAB for mapping the vibration and surface roughness
obtained. The vibration values are given as input to the model and surface roughness is estimated. The results
are encouraging the average percentile error is as low as 0.06. This model can be developed and implemented in
open architectural machine tools for estimating the surface finish automatically without any human intervention.
VII. Conclusion:
In this work an attempt has been made to develop an intelligent system for open architectural machine
tools for estimating the machining performance from online measured vibration. This system will help in
reducing the human interventions, efforts, cost, ideal time and even the process being nondestructive no
scratches are formed unlike conventional stylus probe instruments. As vibration signal can be measured online
while machining operation is being performed. The signal can be sent to the modules that are developed in open
architectural machine tools to monitor and control the machine tool by adjusting the cutting parameters in order
to achieve the desired surface finish.The model estimated with better accuracy and can be used in developing
Rapid-Cam approaches where the product design to the final product can be prototyped in an individual
machine tool.
References
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