Utilizing Predictive Modeling for Bearing Supplier Decision Making
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Transcript of Utilizing Predictive Modeling for Bearing Supplier Decision Making
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Utilizing Predictive Modeling for Bearing
Supplier Decision Making
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Presenter
Dr. Elon Terrell
Computational [email protected]
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Challenge of Machinery OEMs in Bearing Selection
• Selecting bearing that meets design goals while balancing performance and cost
• Standard selection criteria include– Dimensional constraints: inner bore, outer bore, and
width– Tolerance: dimensional accuracy and operating
tolerances– Rigidity: Elastic deformation occurs along the contact
surfaces of a bearing’s rolling elements and raceway surfaces
– Load capacity
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Bearing Selection: Model Designators
Bearing models have standard designators that are universal to all major manufacturers
[code for bearing type][code for bearing cross section][code for bore size]
Cylindrical roller bearingLips on outer ring
Width series 0
Diameter series 3
70 mm bore (14x5)
NU 2 143
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Additional Challenge: Supplier Selection
Since bearing models are mostly standardized across manufacturers, which supplier to choose for a particular application?
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Classic Approach to Life Rating
Static load rating, C, is defined as the static load which the bearing can carry for 1,000,000 revolutions with 10% probability of fatigue failure
Bearing life, in millions of revolutions with 10% probability of fatigue failure:
p
P
CL
10
P = equivalent bearing load, kNp = 3 for ball bearings 10/3 for roller bearings
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Empirical Adjustments to Life Ratings
Adjustments are made to life ratings based upon materials and operating conditions
aM = Adjustment for material - Factor of 1.0 for vacuum-degassed steels - Factor for premium steels is 0.6-1.0
aL = Adjustment for lubrication conditions - Determined by lubricant film parameter, Λ = h/Rq
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Limitations of Classic Approach
• Lack of accounting of varying operating conditions– Operating temperature– Misalignment
• Lack of accounting of internal features– Surface finish (roughness,
skewness, and kurtosis)
– Surface coatings– Roller crowning– Internal clearances
Ground Finish
Superfinish
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Limitations of Classic Approach
Lack of accounting of material characteristics• Grain size distribution• Presence of inclusions and
defects• Residual stress distribution
100X - Case-Core Transition
1000X - Core
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Case Study: Clipper Liberty 2.5MW Wind Turbine
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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System-Level Loads Analysis
Power Flow DiagramGearbox Diagram
Input Shaft
Output Shaft 1
Output Shaft 2
Output Shaft 3
Output Shaft 4
Intermediate Shaft
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Equivalent Bearing Models from Two Suppliers
Item Supplier A Supplier B
Bore, d (mm) 170 170
Outside Diameter, D (mm) 360 360
Width, B (mm) 120 120
Static load rating, C (kN) 2040 2110
L10 life at P = 1500 MPa 3e9 cycles
3e9 cycles
Supplier A
Supplier B
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Internal Geometry Comparison
1. Rollers of the Supplier B bearing were 2mm larger in diameter than those of Supplier B.
2. The inner and outer races of the Supplier B bearing had a higher crown profile than that of Supplier A.
Supplier A – Inner Race
Supplier A – Outer Race
Supplier B – Inner Race
Supplier B – Outer Race
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Component-Level Loads Analysis
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Component-Level Loads Analysis
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Optical Profilometry for Surface Characterization
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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• All surfaces are rough• Characteristics of the surface roughness height
distribution determine the contact behavior
Surface Roughness Modeling
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Surface Roughness MeasurementPart Sq (µm) Sa (µm) Ssk Sku
Supplier A – Outer Race 0.1839 0.1219 -2.3039 17.4057
Supplier A – Inner Race 0.5505 0.3825 -1.8687 9.9358
Supplier B – Outer Race 0.4658 0.3757 -0.1616 4.4789
Supplier B – Inner Race 0.4214 0.3277 0.3945 32.447
Supplier B - Outer RaceSupplier A - Outer Race
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Higher Retained Austenite
Supplier BSupplier A
Material Characterization
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Deterministic Mixed-EHL
Physics-based modeling of interfacial surface contact, frictional heating, and lubrication
Contact Pressure
Asperity Contacts
Lubricant Pressurizatio
n
Surface Deflection
Piezoviscosity
Asperity Contact
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Contact Surface
Contact Surface
Subsurface crack network Surface pit
Contact Surface
Bearing Fatigue Life Predictions Simulation of Damage within Material Microstructure
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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Comparison with Field Observations
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
1.00E+07 1.00E+08 1.00E+09 1.00E+10 1.00E+11
P max
(MPa
)
Number of Cycles
Supplier A - Prediction
Supplier B - Prediction
Supplier A - Field Observations
Supplier B - Field Observations
- Field observations were consistent with Sentient’s findings, showing an improvement in Suppler B bearing life over that of Supplier A.
- Life improvements attributed to differences in internal geometry and material quality
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Summary• Although bearing model designators are standardized,
bearing selection must go beyond consideration of external form factor alone
• Traditional life rating techniques do not account for internal geometry and material variations between suppliers
• Material, surface, lubrication, and operating conditions taken into account in Sentient’s approach towards bearing selection
• Results of Sentient’s analysis agrees with field observations, showing the discrepancy between suppliers of seemingly identical bearings
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015