a5c-ai Fatigue Life Predictor Skill
Purpose
The Fatigue Life Predictor Skill estimates fatigue life of medical implants and load-bearing devices using established methodologies per ASTM and ISO standards, supporting design verification and regulatory submissions.
Capabilities
- S-N curve generation and analysis
- Strain-life fatigue modeling
- Multiaxial fatigue assessment
- Fretting fatigue evaluation
- Corrosion fatigue considerations
- Goodman diagram construction
- Run-out criteria application
- Notch sensitivity analysis
- Statistical treatment of fatigue data
- Design allowable determination
- Fatigue test correlation
Usage Guidelines
When to Use
- Predicting implant fatigue life
- Designing fatigue testing protocols
- Correlating FEA with bench testing
- Supporting design verification
Prerequisites
- Stress analysis completed
- Material fatigue properties available
- Loading spectrum defined
- Surface finish characterized
Best Practices
- Use appropriate fatigue methodology for loading type
- Account for mean stress effects
- Consider physiological environment effects
- Correlate predictions with bench testing
Process Integration
This skill integrates with the following processes:
- Finite Element Analysis for Medical Devices
- Orthopedic Implant Biomechanical Testing
- Design Control Process Implementation
- Verification and Validation Test Planning
Dependencies
- fe-safe software
- ANSYS nCode
- ASTM F1717/F2077 standards
- Material fatigue databases
- FEA stress results
Configuration
fatigue-life-predictor:
methodologies:
- stress-life
- strain-life
- fracture-mechanics
loading-types:
- constant-amplitude
- variable-amplitude
- multiaxial
mean-stress-corrections:
- Goodman
- Gerber
- Morrow
environment:
- air
- saline
- body-fluid
Output Artifacts
- Fatigue life predictions
- S-N curves
- Goodman diagrams
- Safety factor calculations
- Test correlation reports
- Design recommendations
- Statistical analysis results
- Regulatory submission summaries
Quality Criteria
- Methodology appropriate for loading conditions
- Material data from validated sources
- Mean stress effects properly accounted
- Environmental factors considered
- Predictions correlated with testing
- Documentation supports regulatory review