Utilizing Predictive Modeling for Bearing
Supplier Decision Making
Presenter
Dr. Elon Terrell
Computational [email protected]
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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
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
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
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
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
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
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
Case Study: Clipper Liberty 2.5MW Wind Turbine
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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
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
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
Component-Level Loads Analysis
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
Component-Level Loads Analysis
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
Optical Profilometry for Surface Characterization
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
• 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
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
Higher Retained Austenite
Supplier BSupplier A
Material Characterization
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015
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
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
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
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
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