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How to Build a Digital Clone Model

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Engineers, tribologists, and material scientists who work with rotating equipment and components should watch this webinar. An example of a bearing and a gear in a gearbox will be shown.

- How the model is built and parameterized
- What inputs go into the model
- How the model is deployed to solve problems in the Energy, Transportation, Industrial and Aerospace markets

View Webinar Recording at:
http://sentientscience.com/resource-library/videos/webinar-recordings/material-sciences-based-predictive-models-step-step-demonstration-sentient-builds-digitalclone-model/

Published in: Engineering
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How to Build a Digital Clone Model

  1. 1. Using Material Science to Build a Predictive Model of Gearbox Life
  2. 2. Host Natalie Hils Manager of Revenue Marketing nhils@sentientscience.com +1 716.807.8655 Material Science-based Modeling to Predict Life of Gearbox
  3. 3. Sentient Science’s Webinar Series Material Science-based Modeling to Predict Life of Gearbox We want to hear from you! Request a Free Demo of DigitalClone®
  4. 4. Webinar Instructions Material Science-based Modeling to Predict Life of Gearbox
  5. 5. Presenter Edward Wagner Chief Digital Officer ed@sentientscience.com +1 781.929.9295 Material Science-based Modeling to Predict Life of Gearbox
  6. 6. Trusted 3rd Party Uniquely Built with Operators Material Science-based Modeling to Predict Life of Gearbox
  7. 7. Applying Material Science & Computational Testing to Determine Component & System Failure Rates Introducing Sentient Science Material Science-based Modeling to Predict Life of Gearbox 2001-2016 2010 2014 April 2016 April 2016
  8. 8. Sentient Science Family Worlds Most Tested Products Products with the Lowest Cost of Operation Material Science-based Modeling to Predict Life of Gearbox
  9. 9. We Apply Material Science & Computational Testing to Determine Component & System Failure Rates Material Science-based Modeling to Predict Life of Gearbox
  10. 10. This means you now have much more information on asset life, life extension & financial impact Material Science-based Modeling to Predict Life of Gearbox Earliest Failure Reporting and Actions Known Component Failure Accurate Financial Planning 18 month Rolling O&M Forecasting CBM Optimization Risk Management
  11. 11. Poll #1 Material Science-based Modeling to Predict Life of Gearbox
  12. 12. Presenter Material Science-based Modeling to Predict Life of Gearbox Dr. Raja V. Pulikollu VP Implementations, Chief Materials Scientist rpulikollu@sentientscience.com +1 937.241.1144
  13. 13. How We Make a Materials-Based Prognostics Model 1. Determine the Critical Components Driving Gearbox Life 2. Characterize & Compare Microstructure Material Models 3. Apply Computational Tribology Simulation 5. Predict Failure Mode Outcomes in Repeated Steps 4. Simulate Stress in Microstructure to Predict Crack Initiation & Propagation 6. Report on Damage Mode and Fatigue Life Distribution Material Science-based Modeling to Predict Life of Gearbox
  14. 14. Identify Component Hot Spots DigitalClone® Step 1: Determine the Critical Components Driving Gearbox Life Material Science-based Modeling to Predict Life of Gearbox • Build computational models of different components in a gearbox • Analyze stresses translated from system/component loads • Determine high stress regions of component 1 2 3 4 5 6 • Material type (deterministic) • Engineering drawings, tolerances (deterministic) • CAD/FEA model (deterministic) • Loading conditions such as constant amplitude, and variable amplitude (deterministic) Input (deterministic): • Boundary conditions • Elastic modulus • Poisons ratio • Density • Stress-strain data Output (deterministic): • Loads/stress values at critical locations • Displacements • Stress volume
  15. 15. Case Study #1: Wind Turbine Gearbox Supplier Differentiation Material Science-based Modeling to Predict Life of Gearbox Supplier A Supplier B Supplier C Supplier D 5 years 10 years 15 years 20 years Supplier A Supplier B Supplier C Supplier D Most Critical Component High Speed Shaft Downwind Bearing Planetary Gear Bearing High Speed Shaft Downwind Bearing Ring Gear Goal: Predict gearbox life cycle costs based on design and operating conditions. Reduce O&M costs Result: DigitalClone predicted gearbox life, critical components and Supplier ranking: Supplier C > Supplier B > Supplier A > Supplier D Gearbox Supplier C is Superior Compared to Other Suppliers
  16. 16. DigitalClone Step 2: Characterize & Compare Microstructure Material Models Build Material Microstructure Models 1 2 3 4 5 6 • Characterize Material Microstructure for new materials • Add/acquire properties from our material library • Evaluate heat treatments, manufacturing processes Material Science-based Modeling to Predict Life of Gearbox Input: • Retained Austenite, Wt % (probabilistic) • Residual stress profile (deterministic) • Grain size (probabilistic) • Grain orientation (probabilistic) • Inclusion size, density (probabilistic) Output: • Number of grains (probabilistic) • Grain boundary (GB) locations (probabilistic) • Node co-ordinates within grains and at G.B’s (probabilistic) • Inclusion locations (probabilistic)
  17. 17. Bearing Supplier D is Superior Compared to Other Suppliers Case Study #2: Automotive Drives System Bearing Supplier Comparison Material Science-based Modeling to Predict Life of Gearbox Goal: Bearing supplier ranking based on material quality and life. Demonstrate Sentient can reduce physical testing through computational testing Result: Sentient predicted bearing life and ranking correlated with test data OEM selected Supplier D for production Bearing A Version 1 Bearing A Version 2 Bearing B Poor Quality Bearing B Good Quality Bearing C Bearing D Supplier Ranking Supplier Test Data L10 Life, Hours Sentient Predictions L10 Life, Hours 2 Supplier A Ver1 & Ver 2 978.00 (Ver 1&2) 622.58 (Ver 1) 716.93 (Ver 1&2) 764.25 (Ver 2) 4 Supplier B with & without Carbides 308.00 (with and without Carbides) Without carbides - 653.90 370.26 (with and without Carbides)With carbides - 130.45 3 Supplier C 380.00 380.39 N/A 1 Supplier D 1056.0 835.93 N/A
  18. 18. 1 2 3 4 5 6 Build Surface Traction Models • Characterize surface profile and treatments • Acquire/add lubricant properties from our lubricant library • Use Mixed Elastohydrodynamic (EHL) model to generate surface tractions Ground Finish Superfinish Input: • Contact pattern and pressure data (deterministic) • Gear/bearing surface roughness data (probabilistic) • Oil properties (Density vs. Temp, Viscosity vs. Temp (deterministic) Output: • Traction profile (probabilistic) • Coefficient of Friction (probabilistic) • Film thickness (probabilistic) • Asperity contact loading (probabilistic) Material Science-based Modeling to Predict Life of Gearbox DigitalClone Step 3: Apply Computational Tribology Simulation
  19. 19. Sensitivity Analysis – Surface Finish and Oil Quality Case Study #3: Wind Turbine Main Bearing Life Extension Material Science-based Modeling to Predict Life of Gearbox Goal: Main bearing life prediction and life extension through prognostics Result: Duty cycle predictions indicate that L10 fatigue life will improve to 10 yrs. by changing to better lubricant, and to 13.7 yrs. by implementing superfinish 3 Parameter Weibull Reliability Analysis L10 Life L15 Life L17 Life Conservative Case Best Case Conservative Case Best Case Conservative Case Best Case Baseline 7.6 yrs. >20 yrs. 15.5 yrs. >20 yrs >20 yrs >20 yrs Super Bearing 18.4 yrs. >20 yrs. >20 yrs. >20 yrs. >20 yrs. >20 yrs. Overall, L10 fatigue life improved by a factor of 2.42 for Sentient recommended bearing configuration
  20. 20. Material Microstructure Response 1 2 3 4 5 6 • Apply bulk stresses and surface tractions to microstructure Model • Determine material response through damage accumulation, crack nucleation and propagation • Iterate the microstresses and material response Material Science-based Modeling to Predict Life of Gearbox Output: • Crack nucleation (failed nodes) location (probabilistic) • Number of crack nucleation (probabilistic) Input: • Grain Size (probabilistic) • Grain Orientation (probabilistic) • Defect size (probabilistic) • Residual stress (deterministic) DigitalClone Step 4: Simulate Stress in Microstructure to Predict Crack Initiation & Propagation
  21. 21. Calculate Time to Failure 1 2 3 4 5 6 • Determine short crack growth from initiation point • Determine the failure mode and crack patterns • Predict component life (single sample) Material Science-based Modeling to Predict Life of Gearbox Output: • Cracks (failed nodes) location (probabilistic) • Number of cracks (probabilistic) • Crack length (probabilistic) • Grain/material loss (probabilistic) Input: • Grain size (probabilistic) • Grain orientation (probabilistic) • Defect size (probabilistic) • Residual stress (deterministic) DigitalClone Step 5: Predict Failure Mode Outcomes in Repeated Steps
  22. 22. Component Failure Prediction 1 2 3 4 5 6 • Consider different input parameters and their variability • Evaluate 60 simulations per condition to determine probability • Evaluate variability with Weibull theory Input: • # of components simulated (deterministic) Material Science-based Modeling to Predict Life of Gearbox Output: • Primary and secondary damage modes • Probability of failure • Failure life distribution • Crack nucleation and failure life (probabilistic) DigitalClone Step 6: Report on Damage Mode & Fatigue Life Distribution
  23. 23. MCP Rating and Overload Effects Case Study #4: DigitalClone for Rotorcraft Drives System Designs & Operations Material Science-based Modeling to Predict Life of Gearbox Goal: Assist OEMs in increasing the gearbox ratings, safe design, operation and maintenance of rotorcrafts at pilot level and various maintenance levels Result: Prognostic model results were demonstrated and designed for integration with onboard and offboard elements of OEMs health monitoring/management systems. Contact Fatigue Bending Fatigue Nominal Loads Overloads
  24. 24. Information Needed To Build Prognostic Model Computational Test Lab Material Science-based Modeling to Predict Life of Gearbox 1. Technical Drawings • 3D CAD or prints, showing full details of each component including dimensions and references to bulk material properties 2. Lubricant Properties • Absolute viscosity vs. temp, Pressure-viscosity coefficient v. temp 3. Bulk Material Properties • Young's modulus, Poisson's ratio, Density, Yield Strength, Stress/strain curve 4. Microstructure properties (Sentient can obtain from customer provided samples) • Micrographs and fracture surface images, Grain size distribution (mean/variance), Inclusion properties (mean size/variance, density, type), Residual stress profile (if applicable), Process for material treatment (heat treatment process, etc.) 5. Surface Finish (Sentient can obtain from customer provided samples) • Rq, Sk, Ku, Autocorrelation X/Y, 3D roughness height maps, Run-in surfaces as available 6. Operating Conditions • Load, Speed, Operating temperature
  25. 25. Poll #2 Material Science-based Modeling to Predict Life of Gearbox
  26. 26. Summary Material Science-based Modeling to Predict Life of Gearbox 1. Material-based DigitalClone is mainly used for wind turbine, automotive and rotorcraft drive system life prediction and life extension 2. DigitalClone simulator for new and aftermarket product development, maintenance, supplier comparisons, 3rd party health projections • Expedite drive system design and testing at reduced cost • Reduce O&M costs on fielded assets by integrating prognostics with onboard and off board elements of OEMs health monitoring/management systems 3. Simulates “what if” scenarios to optimize performance. DigitalClone parameters can be varied to study the impact on fatigue life • Operating conditions (torque, speed etc.) • System/Component designs (housing, geometry, profile modifications, crowning etc.) • Material quality (microstructure, inclusions, residual stress, surface finish etc.) • Lubricant properties (viscosity, temperature etc.)
  27. 27. DigitalClone® Materials-Based Computational Testing (SaaS) DigitalClone® Live for Operators For Operational Management | Visibility 1 For Asset Management | Visibility 2 For Risk Management | Visibility 3 DigitalClone® Live for Suppliers Interface Suppliers with Operators Sentient Products Material Science-based Modeling to Predict Life of Gearbox
  28. 28. Meet the team! Material Science-based Modeling to Predict Life of Gearbox May 17th-19th West Palm, FL Booth# 508 May 24th-26th New Orleans, LA Booth# 3227 *Introducing new product* DigitalClone® Live for Suppliers
  29. 29. Upcoming Webinars Material Science-based Modeling to Predict Life of Gearbox
  30. 30. Thank you! Using Material Science to Build a Predictive Model of Gearbox Life

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