DOI : 10.17577/IJERTV4IS120620
- Open Access
- Total Downloads : 227
- Authors : Mr. Lalit N. Patil, Prof. A. V. Patil
- Paper ID : IJERTV4IS120620
- Volume & Issue : Volume 04, Issue 12 (December 2015)
- DOI : http://dx.doi.org/10.17577/IJERTV4IS120620
- Published (First Online): 26-12-2015
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Wear Prediction Model for Composite Bearing Balls under Pure Sliding Contact Condition
Mr. Lalit N. Patil
PG scholar, Design Engineering S.S.G.B.C.O.E.T. Bhusawal, Dist. Jalgaon Maharashtra, India.
Prof. A. V. Patil
Associate Professor, Mechanical Engineering Dept.
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Bhusawal, Dist. Jalgaon
Maharashtra, India.
Abstract In todays scenario, maintenance of any machinery is very important in view of downtime of machinery. The bearing sector is one of this examples without which any single rotating machinery work. Present work is focused on prediction of wear for ball materials in case of ball bearing under pure sliding conditions. According to ASTM G99 standard Pin on Disc Apparatus is used to determine relative wear (in micron). The wear is also calculated by measuring weight of balls in grams. Taguchi approach with L9 array is employed to conduct experiments. Engine oil 20W40 is used for lubricating condition. An orthogonal array, Signal to Noise ratio and analysis of variance were employed to investigate wear behavior of composite ball bearing. Finally a model is prepared for Silicon Nitride, Alumina Oxide and Chrome Steel balls.
Keywords Composite Ball Bearing, ANOVA, Silicon Nitride, Taguchi Technique.
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INTRODUCTION
The need for a ball bearing made of a material which has better performance than the standard steel ball bearing will find many applications in mechanical devices. These ball bearings can provide better performance and greater reliability. This study investigates the use of silicon nitride (Si3N4) in the construction of ball bearings. The scope can include using silicon nitride for the balls only or for the balls and race. Wear is measured to investigate performance of balls in case of ball bearing. The challenge is to make the silicon nitride bearings material and processing costs close to that of steel bearings.
The Alumina can be alternate material for balls in case of ball bearing. Many times bearing fails due to wear of balls, therefore this work is based on wear prediction.
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OVERVIEW OF TAGUCHI METHODS Taguchi methods start with an assumption that we are
designing an engineering system – either a machine to perform some intended function, or a production process to manufacture some product or item. Since we are knowledgeable enough to be designing the system in the first place, we generally will have some understanding of the fundamental processes inherent in that system. Basically, we use this knowledge to make our experiments more efficient. We can skip all the extra effort that might have gone in to investigating interactions that we know does not exist.
Without going into the details, it has been shown that this can decrease the level of effort by a factor of ten or twenty and sometimes much more.
Another distinction of Taguchi methods is the recognition that there are variables that are under our control and variables that are not under our control. In Taguchi terms, these are called Control Factors and Noise Factors, respectively.
The Taguchi Method is applied in four steps.
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Brainstorm the quality characteristics and design parameters important to the product/process.
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Design and conduct the experiments.
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Analyze the results to determine the optimum conditions.
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Run a confirmatory test using the optimum conditions.
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Design of Experiments
As per Taguchi approach the test is conducted. Depends upon the number of parameters and the no of levels, the proper L9 orthogonal array is selected.
TABLE I. TEST PARAMETER
Level
Velocity (m/s)
Load (N)
Time (min)
1
V1=7
L1=10
T1=30
2
V2=10
L2=60
T2=60
3
V3=14
L3=120
T3=90
TABLE II. L 9 ORTOGONAL ARRAY
L9 Test
P1
P2
P3
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
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Designing the Experiments
Before designing an experiment, knowledge of the product/process under investigation is of prime importance for
identifying the factors likely to influence the outcome. The aim of the analysis is primarily to seek answers to the following three questions:
-
What is the optimum condition?
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Which factors contribute to the results and by how much?
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What will be the expected result at the optimum condition?
-
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EXPERIMENTAL SETUP
In tribometer TR-20, Counter surface of known material is to be fixed. Ball mounted on Stiff lever having frictionless force transducer. The deflection of the highly stiff elastic arm, without parasitic friction, insures a nearly fixed contact point and thus a stable position in the friction track. The friction coefficient is determined during the test by measuring the deflection of the elastic arm. Wear coefficients for the ball and disc material are calculated from the volume of material lost during the test. This simple method facilitates the study of friction and wears behavior of almost every solid-state material combination with or without lubricant. Furthermore, the control of the test parameters such as speed, contact pressure and varying time allow a close reproduction to the real life conditions of practical wear situations.
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Specification of Tribometer
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Ball Size 10 mm diameter
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Ball holder for 10 mm diameter ball
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Disc Size 165 mm dia. X 8 mm thick
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Wear Track Diameter (Mean) 10 mm to 140 mm
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Sliding Speed Range 0.5 m/sec. to 15 m/sec.
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Disc Rotation Speed 200-2000 RPM
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Sensor Proximity Sensor
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Normal Load 200 N maximum.
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Frictional Force 0-200 N, digital readout, recorder output
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Wear Measurement Range 4 mm, digital readout, and recorder output
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Wear Sensor Spec. LVDT
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Power 230 V, 15A, 1 Phase, 50 Hz
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Power for motor 1.5 kw
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Lubrication Motor 0.1 Hp, 0.48A, 230V.
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Oil Recirculation Unit 3 liter capacity with gear pump, 0.1Hp
Fig. 1 Top View of Tribometer
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Test Procedure
This test is conducted as per G99 Standard of ASTM. Chrome Steel discs are polished with metallographic abrasive papers (C-400) and (C-600) respectively. Chrome Steel disc
rotating at a selected speed slide against a ball according to velocity track diameter of ball on disc is varied accordingly. This pre-rubbing process ensured a full contact of the ball and disc surfaces. The surface roughness Ra of disc speimens is 0.090.11m. All the specimens were manually cleaned in petrol and then thoroughly dried. The friction and wear tests were performed at room temperature (280 C) in atmosphere. Applied loads ranged from 10 N to 120N and rotation speeds of discs ranged from 7m/s to 14m/s, time ranged from 30 to 90 minute, and the sliding distance was varied accordingly. The Servo engine oil (20W40) oil is used at flow rate of 50 ml/min on the rubbing surfaces using oil lubrication system during the wet test. It is ensured that lubrication will be continuously between Pin and counterface during the wet test.
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EXPERIMENTAL RESULTS
The trials are conducted as per orthogonal array L9 for dry and wet conditions separately. Following results are obtained & the same are used for ANOVA.
Fig. 2 Wear Results of various Trials
It is observed that wear of chrome steel ball is much greater than Silicon Nitride and Alumina balls.
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ANALYSIS OF VARIANCE
-
Chrome Steel ball Material
ANOVA is done in MINITAB R15 software. Following results were obtained. This analysis was carried out for level of confidence 95%.
The regression equation is,
W = 0.105 + 0.00051 V + 0.000591 L + 0.000161 T
S = 0.0105521 R-Sq = 92.1% R-Sq(adj) = 87.4%
TABLE III. MODEL ANOVA FOR CHROME STEEL
Source
DF
SS
MS
F
P
Regression
3
0.0065093
0.0021698
19.49
0.003
Residual Error
5
0.0005567
0.0001113
Total
8
0.0070660
Source
DF
SS
% Contribution
V
2
0.000098
1.3860
L
2
0.006613
93.589
T
2
0.000321
4.5428
TABLE IV. PARAMETER ANOVA FOR CHROME STEEL
Main Effects Plot for Means
Data Means
C. Silicon Nitride
Following results were obtained. This analysis was carried out for level of confidence 95%.
The regression equation is,
W = 0.0639 + 0.00027 V + 0.000307 L – 0.000033 T
S = 0.0105336 R-Sq = 75.7% R-Sq(adj) = 61.1%
Source
DF
SS
MS
F
P
Regression
3
0.0017261
0.0005754
5.19
0.054
Residual Error
5
0.0005548
0.0001110
Total
8
0.0022809
TABLE VII. MODEL ANOVA FOR SILICON NITRIDE
V
L
0.180
0.165
0.150
Mean of Means
0.135
0.120
7
0.180
.
0.165
0.150
0.135
0.120
30
10 14
T
60 90
10 60
120
TABLE VIII. PARAMETER ANOVA FOR SILICON NITRIDE
Source
DF
SS
% Contribution
V
2
0.000010
0.43840
L
2
0.002158
86.88
T
2
0.000012
0.52608
Fig. 3 Effect of Parameter on Wear of Chrome Steel
Main Effects Plot for SN ratios
Data Means
V L
18
17
Mean of SN ratios
16
15
0.12
0.11
0.10
Mean of Means
0.09
Main Effects Plot for Means
Data Means
V L
T
7 10
14 10
60 120
0.08
7
10 14 10
60 120
18
17
16
15
30 60 90
0.12
0.11
0.10
0.09
0.08
30
T
60 90
Signal-to-noise: Smaller is better
Fig. 4 S/N Ratio for Chrome Steel
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Aluminium Oxide
ANOVA is done in MINITAB R15 software.
Fig. 5 Effect of Parameter on Wear of Alumina
Main Effects Plot for SN ratios
Data Means
Following results were obtained. This analysis was carried out for level of confidence 95%.
The regression equation is,
W = 0.0884 – 0.000806 V + 0.000350 L + 0.000000 T
S = 0.00674703 R-Sq = 90.9% R-Sq(adj) = 85.5%
TABLE V. MODEL ANOVA FOR ALUMINA
22 V
21
Mean of SN ratios
20
19
18
T
7 10
22
21
20
19
18
30 60
14 10
90
L
60 120
TABLE VI. PARAMETER ANOVA FOR ALUMINA
Signal-to-noise: Smaller is better
Source
DF
SS
MS
F
P
Regression
3
0.00227994
0.00075998
16.69
0.005
Residual
Error
5
0.00022761
0.00004552
Total
8
0.00250756
Fig. 6 S/N Ratio for Alumina
Source
DF
SS
% Contribution
V
2
0.000070
2.7910
L
2
0.002244
89.473
T
2
0.000001
3.98
0.10
0.09
0.08
Mean of Means
0.07
0.06
Main Effects Plot for Means
Data Means
V
L
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The analysis of variance shows that the load (89.47%) and time (3.98%) have significant influence on wear of Aluminiun Oxide ball in wet condition.
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The analysis of variance shows that the load (86.88%) and time (0.52608%) have significant influence on wear of Silicon Nitride ball in wet condition.
7
0.10
0.09
0.08
0.07
0.06
30
10 14 10
T
60 90
60 120
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The regression equation gives the best results for various parameters. This means a considerable saving in cost and time, which could benefit the industry to build more general and particular databases of material properties.
ACKNOWLEDGMENT
Fig. 7 Effect of Parameter on Wear of Silicon Nitride
Main Effects Plot for SN ratios
Data Means
Author Lalit Patil would like to thank all those involved, like respected Guide and HOD Prof. A. V. Patil, (Department of Mechanical Engineering) for his indispensable support, priceless suggestions and valuable time. Also thanks to Prof.
V
24
23
Mean of SN ratios
22
21
20
7 10
T
24
23
22
21
20
30 60
14 10
90
L
60 120
R. B. Barjibhe (Dean Academics), Prof. P. S. Bajaj for their kind co-operation and valuable guidance throughout this work.
REFERENCES
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S. Basavarajappay and G. Chandramohan, 2005, Wear Studies on Metal Matrix Composites: a Taguchi Approach, J. Mater. Sci. Technol., Vol.21 No.6, 2005.
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H. B. Bhaskar & Abdul Sharief, 2012, Dry Sliding Wear Behaviors of Aluminium/Be3Al2(SiO3)6 Composite Using Taguchi Method, Journal of Minerals and Matrials Characterization and Engineering, 2012, 11, 679-684.
Signal-to-noise: Smaller is better
Fig. 8 S/N Ratio for Silicaon Nitride
-
-
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CONFIRMATION TEST
After successful development of model a confirmation test is carried out for validation.
Material
V
(m/s)
L (N)
T
(min)
Wear by Expt. (gm)
Wear by regression (gm)
Chrome
Steel
14
10
60
0.1310
0.12771
Al2O3
14
10
60
0.1035
0.1031
Si3N4
14
10
60
0.0690
0.06877
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CONCLUSION
-
-
The DOE technique was successfully used to study the dry and wet sliding wear of ball bearing materials.
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The analysis of variance shows that the load (93.58%) and time (4.54%) have significant influence on wear of chrome steel ball in wet condition.
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L. Wang, R.J.K. Wood, T.J. Harvey, S. Morris, H.E.G. Powrie & I. Care, 2003, Wear performance of oil lubricated silicon nitride sliding against various bearing steels, Elsevier Wear 255(2003) 657-668.
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Varade B. V., Dr. Kharde Y. R., 2012, Prediction of specific wear rate of glass filled PTFE composites by Artificial Neural Networks and Taguchi Approach, International Journal of Engineering Research and Applications, ISSN: 2248-9622 Vol. 2 Issue 6 PP 679-683.
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Zhenyu Jiang, Zhong Zhang, Klaus Friedrich, Prediction on wear properties of polymer composites with artificial neural networks, Composite science and technology, Institute for Composite Materials, University of Kaiserslautern, Germany, 2 October 2006.pp 168-176.
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R. J. Parker & E. V. Zaretsky, 1975, Fatigue life of high-speed ball bearings with silicon nitride balls, ASME, 350/July 1975.
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Cheng Junsheng, Yu Dejie & Yang Yu, 2004, A fault diagnosis approach for roller bearings based on EMD method and AR model, Elsevier, Mechanical Systems and Signal Processing 20 (2006) 350 362.
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H. Unal, U. Sen., A. Mimaroglu, An approach to friction and wear properties of polytetrafluoroethylene composite Material and Design, Adapazari, Turkey, 16 February 2005, pp.694-699.
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Jaydeep Khedkar, Ioan Negulescu, Efstathios, Meletis, Sliding wear behavior of PTFE composites, Wear, Department of Mechanical Engineering, Louisiana State University, 25 June 2001.pp.361-369.