Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Fatigue

Fatigue

Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Advances in Fatigue Analysis and Numerical Simulation in Engineering Materials.

Materials (Basel, Switzerland)·2026

Same author

Atomistic-Based Fatigue Property Normalization Through Maximum A Posteriori Optimization in Additive Manufacturing.

Materials (Basel, Switzerland)·2025

Same author

Methodology for Hydrogen-Assisted Fatigue Testing Using In Situ Cathodic Charging.

Materials (Basel, Switzerland)·2025

Same author

Separation of Damage Mechanisms in Full Forward Rod Extruded Case-Hardening Steel 16MnCrS5 Using 3D Image Segmentation.

Materials (Basel, Switzerland)·2024

Same author

Characterization of Interfacial Corrosion Behavior of Hybrid Laminate EN AW-6082 ∪ CFRP.

Materials (Basel, Switzerland)·2024

Same author

Comparison of Various Intrinsic Defect Criteria to Plot Kitagawa-Takahashi Diagrams in Additively Manufactured AlSi10Mg.

Materials (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: May 10, 2025

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Published on: March 13, 2018

AI-Powered Very-High-Cycle Fatigue Control: Optimizing Microstructural Design for Selective Laser Melted Ti-6Al-4V.

Mustafa Awd^1,2, Frank Walther³

¹Institute for Informatics and Automation (IIA), Bremen City University of Applied Sciences (HSB), Flughafenallee 10, D-28199 Bremen, Germany.

Materials (Basel, Switzerland)

|April 24, 2025

Summary

Machine learning optimizes additive manufacturing for durable, fatigue-resistant components. This approach enhances material properties and reduces design cycles by over 50% for critical aerospace and biomedical applications.

Keywords:

additive manufacturing machine learning microstructural optimization process parameter optimization very high cycle fatigue (VHCF) resistance

More Related Videos

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Published on: December 8, 2015

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

Related Experiment Videos

Last Updated: May 10, 2025

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Published on: March 13, 2018

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Published on: December 8, 2015

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

Area of Science:

Materials Science and Engineering
Mechanical Engineering
Computational Science

Background:

Additive manufacturing (AM) presents opportunities for advanced material design.
Optimizing material properties for fatigue resistance is crucial for critical applications.
High cycle fatigue (HCF) testing is essential for assessing material durability.

Purpose of the Study:

To integrate machine learning (ML) into AM for enhanced material properties and fatigue resistance.
To explore mechanistic ML techniques for tailoring microstructures.
To accelerate the design and production of reliable, high-performance components.

Main Methods:

Utilized ultrasonic fatigue tests to gather very high cycle fatigue data (up to 1x10^10 cycles).
Applied mechanistic machine learning models to predict fatigue thresholds and optimize process parameters.
Analyzed microstructural features like grain orientation and phase uniformity.

Main Results:

ML models reduced design iteration cycles by over 50%.
Fatigue crack propagation resistance improved by 20-30% through microstructural refinement.
Achieved 15% weight reduction and improved yield strength in ML-designed metamaterials.
Identified key process parameters (temperature gradients, cooling rates) influencing microstructural evolution in titanium and aluminum alloys.

Conclusions:

ML integration in AM significantly enhances fatigue life and reliability for critical components.
ML enables the design of lightweight, high-strength metamaterials for diverse applications.
This research paves the way for intelligent, adaptive manufacturing systems with improved performance and efficiency.