A Step By Step Approach to Predict Fatigue, Wear Failure and Remaining Useful Life in Mechanical...

Developing a Multi-Physics

DigitalClone® Model

October 31, 2014

Sentient Science: Multi-Physics Modeling

What is Prognostics?

Prognostics is an engineering discipline focused on predicting the time

at which a system or a component will no longer perform its

intended function

This lack of performance is most often a failure beyond which the

system can no longer be used to meet desired performance

It’s a dynamic process where predictions get updated with an

appropriate frequency as more observation data become available from

an operational system

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How Does Sentient Use Prognostics?

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What Technology enables Model-based

Prognostics?

Capability: Highly accurate reliability and performance prediction

– Holistic approach – considers multi-body dynamics, tribology, material

science, and real world variability

predict loads, life, and performance of

complex systems

predict impact of feature level design

factors on component performance

complete solution for optimal lifecycle

management of fielded assets

Component Life Prediction (CLP) Technology Overview

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2

3

4

5

6

Determine Component Hot Spot

Build Material Microstructure Models

Build Surface Traction Models

Material Microstructure Response

Calculate Time to Mechanical Failure

Generate Response Surface

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• Build computational models of different components

• Analyze stresses translated from system loads

• Determine high stress regions of component

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• Extract:

• Contact pressures

• Relative velocities

• Curvatures

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Contact pattern

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• Characterize Material Microstructure for new materials in bearing, gear, or spline

• Or - Acquire properties from our material library

• Evaluate heat treatments, manufacturing processes

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Pinion

EDM

sectioning

Samples for

microstructure

analysis

Residual stress

analysis

• Gears: Measure micro-hardness profile from the tooth surface into the depth and manufacturing-induced residual stress profile

• Bearings: Measure same for rolling elements, inner and outer race materials

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• Characterize the different between case material and core material

• Measure grain size of materials, inclusions, defects

• For fielded components, measure damage accumulation

Case Core

Gear Tooth

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• Use Voronoi Tessellation to numerically represent microstructure

• Generation process is random in nature and requires some domain simplification


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• Instead of characterization, purchase existing material microstructures for your private Material Library

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• Characterize surface profile and treatments

• Acquire lubricant properties from our lubricant library

• Use Mixed Elastohydrodynamic (EHL) model to generate surface tractions

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SuperfinishGround Finish

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• Measure surface roughness using optical or contact profilometry, in-house

• Statistically identical surfaces are numerically generated for use in lubrication analysis

• Roughness profiles are superimposed on the mating surface base geometry



• Use Mixed Elastohydrodynamic (EHL) solver to account for microasperities

• Determine the performance of surface finishes during the generation, sustainment, and/or failure of an EHL film

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• Current R&D to incorporate heat generation/efficiency analysis of gears and bearings with different surface finishes

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• Current R&D to incorporate heat generation/efficiency analysis of gears and bearings with different surface finishes

• Accurate velocity fields are necessary to maintain continuity

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• Lubricant Properties as Inputs

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Material Microstructure Response

• Apply bulk stresses and surface tractions to microstructure Model

• Determine material response through damage accumulation and crack nucleation and propagation

• Iterate the microstresses and material response

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• Determine short crack growth from initiation point

• Determine the failure mode and crack patterns

• Predict component life (single sample) and repeat

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Tanaka (1981)October 31, 2014

Micropitting Fatigue Spalling Fatigue

Bending Fatigue Fretting Fatigue

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• Metallic Wear (Abrasion, Adhesion, Scuffing)

• Corrosion

• Composite Delamination

• Coating Degradation

• Fretting Wear

• White Layer Etching

Failures Modes in R&D:



• Consider different input parameters and their variability

• Evaluate 20-30 samples (with different microstructure distribution) per condition to determine probability

• Evaluate variability with Weibull theory

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• NASA has performed 50 bending and surface fatigue tests

• DigitalClone evaluated 23 samples per condition to determine probability

• NASA Data

• Townsend (1995) TM-107017

• Townsend (1982) TP-2047

• Krantz (2004) ASME

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Parameter NASA

Sentient

DigitalClone

Weibull

Slope 2.2 2.78

L10 22 27

L50 52 54

L90 89 84

What is DigitalClone System?

Solves Application Specific

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• Put DigitalClone Component into multi-body dynamic models

• Analyze each component to determine life of full system

• Evaluate misalignment, critical components, and re-designs

predict loads, life, and performance of complex systems

What is DigitalClone Live?

Solves Application Specific

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• Connect DigitalClone System model to fielded assets

• Acquire new operating conditions from sensors and SCADA

• Re-evaluate failure risk and run ‘what-if’ scenarios to control

complete solution for optimal lifecycle management of

fielded assets

What is DigitalClone?

Solve Application Specific Challenges over the Industrial Industrial Internet

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Prognostics Technologies: Introduction to DigitalClone Component 30

Solve Failures in Testing,

Production, or Field

Develop New Products

based on Performance

Manage and Extend the Life

of Fielded Assets

Optimize Testing Programs’

Time and Spend

Developing a Multi-Physics

DigitalClone® Model

A Step By Step Approach to Predict Fatigue, Wear Failure and Remaining Useful Life in Mechanical...

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Transcript of A Step By Step Approach to Predict Fatigue, Wear Failure and Remaining Useful Life in Mechanical...