Fatigue failure of structures used in transportation, industry, medical equipment, and electronic components needs to build a link between cutting-edge experimental characterization and probabilistically grounded numerical and artificially intelligent tools. The physics involved in this process chain is computationally prohibitive to comprehend using traditional computation methods. Using machine learning and Bayesian statistics, a defect-correlated estimate of fatigue strength was developed. Fatigue, which is a random variable, is studied in a Bayesian-based machine learning algorithm. The stress-life model was used based on the compatibility condition of life and load distributions. The defect-correlated assessment of fatigue strength was established using the proposed machine learning and Bayesian statistics algorithms. It enabled the mapping of structural and process-induced fatigue characteristics into a geometry-independent load density chart across a wide range of fatigue regimes.
Author(s): Mustafa Mamduh Mustafa Awd
Series: Werkstofftechnische Berichte │ Reports of Materials Science and Engineering
Publisher: Springer Vieweg
Year: 2023
Language: English
Pages: 288
City: Wiesbaden
Foreword
Preface
Abstract
Kurzfassung
Contents
Abbreviations
List of symbols
Latin symbols
Greek symbols
List of Figures
List of Tables
1 Introduction and Objectives
2 Background on Process-Property Relationship
2.1 Fatigue Strength of Additively Processed Al and Ti Alloys
2.1.1 Aluminum Alloys (Al)
2.1.2 Titanium Alloys (Ti)
2.2 Fatigue Analysis and Modeling
2.2.1 Deterministic and Probabilistic Approaches to Fatigue
2.2.2 Process and Microstructural Simulation
3 Training and Testing Data
3.1 Aluminum and Titanium Lightweight Alloys
3.1.1 Aluminum (Al) Alloys
3.1.2 Titanium (Ti) Alloys
3.2 Additive Processing and Functional Grading
3.2.1 Principles of Additive Manufacturing
3.2.2 Functional Grading and Local Heat Treatments
3.3 Material Characterization on Multiscale
3.3.1 Structural Properties
3.3.2 Microstructural Properties
3.3.3 Quasi-static Properties
3.3.4 Fatigue Strength
4 Estimation of Lifetime Trends Based on FEM
4.1 Linear Elastic Fracture Mechanics
4.1.1 Description of Crack Tip State
4.1.2 Fatigue Crack Propagation
4.2 Extended Finite Element Method
4.2.1 SIF Analysis Using XFEM and Contour Integral Method
4.2.2 Fatigue Crack Propagation with XFEM
4.3 Visualization and Post-processing
4.3.1 Virtual Crack Closure Technique (VCCT)
4.3.2 Level Set Method
4.3.3 Damage Initiation Criteria
4.3.4 Model Establishment and Simulation
4.4 Modeling of Cyclic Deformation
4.4.1 Continuum Material Models
4.4.2 Implementation and Solution Optimization
4.4.3 Multiaxial Deformation Sensitivity
4.5 Shear-based Fatigue Damage Quantification
4.5.1 Fatemi-Socie Planar Fatigue Damage Model
4.5.2 Adaptation of the Model to VHCF Application
5 Bayesian Inferences of Fatigue-related Influences
5.1 Phenomenological Statistical Learning
5.1.1 Extreme Value Statistics in Fatigue
5.1.2 Parameter Estimation
5.1.3 Application of the Scheme
5.2 A Two-parameter Bayesian Inference
5.2.1 Statistical Parameter Mapping
5.2.2 Joint Probability of Fatigue-related Influences
5.2.3 Belief Function of Fatigue Strength
5.2.4 Estimation of Error
5.3 Influence of Structure and Process on Fatigue Strength
5.3.1 Remnant Defects
5.3.2 Width of Prior β Grains
5.3.3 Energy Density Ev
6 Summary and Outlook
6.1 Processing
6.2 Mechanical and Structural Properties
6.3 Fracture Mechanics
6.4 Cyclic Plasticity
6.5 Bayesian Statistics
6.6 Outlook
References
