Utilizing Predictive Modeling for Bearing Supplier Decision Making

Utilizing Predictive Modeling for Bearing

Supplier Decision Making

Presenter

Dr. Elon Terrell

Computational Tribologisteterrell@sentientscience.com

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