Exciting news! ICMD® is expanding its capabilities with the upcoming launch of the Fatigue Toolkit, a cutting-edge solution designed to revolutionize fatigue life prediction.

This powerful new tool will provide engineers and materials scientists with unprecedented insights into fatigue performance, reducing costs, improving safety, and accelerating product development. This proven approach to modeling minimum fatigue life can be combined with limited testing using the Accelerated Insertion of Materials (AIM) methodology on ICMD® to save 70% in testing costs vs a traditional full design of experiments approaches for qualification of fatigue critical materials.

Schedule your ICMD® demo with our team of experts. 

What Makes the Fatigue Toolkit Stand Out?

ICMD®’s Fatigue Toolkit combines physics-based simulations with microstructure and defect digital twins, enabling engineers to improve safety, reduce costs, and make data-driven decisions for material selection, design, and deployment.

Current state-of-the-art fatigue modeling primarily relies on empirical data fitting or linear elastic fracture mechanics, both of which have significant limitations. Empirical models lack predictive capability and require large safety factors, leading to costly overengineering. Fracture mechanics approaches, while useful, only address macroscopic crack propagation after significant damage has already occurred.

The ICMD® Fatigue Toolkit introduces microstructure-sensitive fatigue modeling, using crystal plasticity simulations, to enable engineers to predict crack formation and microstructurally small crack growth phases, which is particularly crucial for high-cycle fatigue applications. By incorporating microstructural attributes, this approach reduces reliance on conservative safety factors, leading to both safer and more cost-efficient engineering solutions.

Key Features & Benefits

• 3D Digital Microstructure Generation – Statistical digital twin of grain size, orientation, morphology, inclusions, and porosity.
• Material Model Calibration – Uses cyclic stress-strain data to enable custom calibration of plasticity models.
• Customizable Loading Scenarios – Supports varied multi-axial or uniaxial strain states, strain ratios, and fatigue cycles.
• Multi-Stage Fatigue Prediction – Tracks fatigue life from crack incubation to long crack growth predicting full fatigue life in high and low cycle fatigue.
• Streamlined CPFEM Integration – Enables computationally efficient, physics-based fatigue simulations.
• Component-Scale Fatigue Analysis – Integrates with finite element simulations for real-world fatigue predictions.

Why It Matters

Traditional fatigue models often rely on large safety factors due to their lack of predictive accuracy, leading to overengineering and increased costs. By incorporating microstructural attributes, the Fatigue Toolkit reduces these uncertainties, allowing for safer and more cost-effective engineering solutions. Whether you’re designing aerospace components, automotive parts, or additively manufactured materials, this toolkit will help you make data-driven decisions with confidence.

Industry Applications & Real-World Impact

ICMD®’s Fatigue Toolkit has already demonstrated success in industry engagements, helping companies optimize material selection, enhance supplier qualification requirements, and accelerate certification processes. Examples include:

• Material Selection Trade Studies – Used in aerospace applications to evaluate high-cycle fatigue performance of various competing alloys, enabling physics-informed material selection.
• Supplier Qualification Optimization – Helps OEMs define more precise qualification requirements by considering microstructural influences on fatigue performance, reducing variability across material suppliers and improving supply chain resilience.

• Accelerated Qualification for Additive Manufacturing – Applied in collaboration with the Air Force Research Laboratory (AFRL) to streamline the qualification of additively manufactured materials, significantly reducing required experiments (shortening timelines from years to months) and cutting qualification costs by up to $2 million.