a step by step approach to predict fatigue, wear failure and remaining useful life in mechanical...
DESCRIPTION
When I request a Multi-physics DigitalClone model from Sentient Science, how exactly does their team develop and use the model? Our technical approach to predict future contact-based fatigue and wear life with DigitalClone prognostic models. In this webinar, you will learn what inputs go into the model, how the model is built and parameterized, and how the model is deployed to solve problems. Engineers, tribologists, and material scientists who work with rotating equipment and components should join this webinar. An example of a bearing and a gear in a gearbox will be shown.TRANSCRIPT
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
October 31, 2014
Sentient Science: Multi-Physics Modeling
How Does Sentient Use Prognostics?
October 31, 2014
Sentient Science: Multi-Physics Modeling
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
1
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
October 31, 2014
Sentient Science: Multi-Physics Modeling
Determine Component Hot Spot
• Build computational models of different components
• Analyze stresses translated from system loads
• Determine high stress regions of component
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
Determine Component Hot Spot
• Extract:
• Contact pressures
• Relative velocities
• Curvatures
1 2 3 4 5 6
Contact pattern
October 31, 2014
Sentient Science: Multi-Physics Modeling
• Characterize Material Microstructure for new materials in bearing, gear, or spline
• Or - Acquire properties from our material library
• Evaluate heat treatments, manufacturing processes
1 2 3 4 5 6
Build Material Microstructure Models
October 31, 2014
Sentient Science: Multi-Physics Modeling
1 2 3 4 5 6
Build Material Microstructure Models
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
October 31, 2014
Sentient Science: Multi-Physics Modeling
1 2 3 4 5 6
Build Material Microstructure Models
• 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
October 31, 2014
Sentient Science: Multi-Physics Modeling
1 2 3 4 5 6
Build Material Microstructure Models
• Use Voronoi Tessellation to numerically represent microstructure
• Generation process is random in nature and requires some domain simplification
Build Material Microstructure Models
October 31, 2014
Sentient Science: Multi-Physics Modeling
• Instead of characterization, purchase existing material microstructures for your private Material Library
1 2 3 4 5 6
Sentient Science: Multi-Physics Modeling
Build Surface Traction Models
• Characterize surface profile and treatments
• Acquire lubricant properties from our lubricant library
• Use Mixed Elastohydrodynamic (EHL) model to generate surface tractions
1 2 3 4 5 6
SuperfinishGround Finish
October 31, 2014
Sentient Science: Multi-Physics Modeling
Build Surface Traction Models
1 2 3 4 5 6
October 31, 2014
• 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
Sentient Science: Multi-Physics Modeling
Build Surface Traction Models
• 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
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
Build Surface Traction Models
• Current R&D to incorporate heat generation/efficiency analysis of gears and bearings with different surface finishes
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
Build Surface Traction Models
• 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
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
Build Surface Traction Models
• Lubricant Properties as Inputs
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
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
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
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
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
Calculate Time to Mechanical Failure
• Determine short crack growth from initiation point
• Determine the failure mode and crack patterns
• Predict component life (single sample) and repeat
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
Calculate Time to Mechanical Failure
1 2 3 4 5 6
Tanaka (1981)October 31, 2014
Micropitting Fatigue Spalling Fatigue
Bending Fatigue Fretting Fatigue
1 2 3 4 5 6
Calculate Time to Mechanical Failure
Sentient Science: Multi-Physics Modeling
October 31, 2014
1 2 3 4 5 6
Calculate Time to Mechanical Failure
Sentient Science: Multi-Physics Modeling
October 31, 2014
• Metallic Wear (Abrasion, Adhesion, Scuffing)
• Corrosion
• Composite Delamination
• Coating Degradation
• Fretting Wear
• White Layer Etching
Failures Modes in R&D:
Sentient Science: Multi-Physics Modeling
Generate Response Surface
• 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
1 2 3 4 5 6
October 31, 2014
Sentient Science: Multi-Physics Modeling
Generate Response Surface
• 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
1 2 3 4 5 6
October 31, 2014
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
October 31, 2014
Sentient Science: Multi-Physics Modeling
• 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
October 31, 2014
Sentient Science: Multi-Physics Modeling
• 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
October 31, 2014
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