Machinery OEMs often have to choose between different bearing suppliers when designing a new device. Although the same bearing model from different suppliers may have the same external form factor and load rating, there are often differences in internal geometry, material quality, and surface treatment from one supplier to another that will cause the performance and expected life to vary in the field. These slides will outline Sentient Science’s approach towards measuring and quantifying these hidden bearing parameters. We show how we combine material, geometry, and surface treatment metrics into high-fidelity predictive models which we use to help OEMs decide which bearing supplier to choose for their application.

1. Utilizing Predictive
Modeling for Bearing
Supplier Decision Making

2. Presenter
Dr. Elon Terrell
Computational Tribologist
eterrell@sentientscience.com
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

3. 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

4. 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 bearing
Lips 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

5. 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

6. 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
C
L 





10
P = equivalent bearing load, kN
p = 3 for ball bearings
10/3 for roller bearings
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

7. 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

8. 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

9. 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

10. Case Study: Clipper Liberty 2.5MW Wind
Turbine
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

11. 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

12. 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

13. 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

14. Component-Level Loads Analysis
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

15. Component-Level Loads Analysis
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

16. Optical Profilometry for Surface
Characterization
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

17. • 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

18. Surface Roughness Measurement
Part 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

19. Higher Retained Austenite
Supplier BSupplier A
Material Characterization
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

20. Deterministic Mixed-EHL
Physics-based modeling of
interfacial surface contact,
frictional heating, and
lubrication
Contact Pressure
Asperity Contacts
Lubricant
Pressurization
Surface
Deflection
Piezoviscosity
Asperity
Contact
Webinar: Utilizing Predictive Modeling for Bearing Supplier Decision Making
May 13, 2015

21. 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

22. 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
Pmax(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

23. 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