Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields

Area of Science:

• Materials Science
• Mechanical Engineering
• Data Science

Background:

• High-fidelity mechanical property prediction models require extensive microstructural data.
• Deformation fields in polycrystalline materials under load are often heterogeneous at micro-scales.
• Spatially correlated 3D microstructure and deformation data are rare but valuable.

Purpose of the Study:

• To present a unique, multi-modal dataset combining 3D microstructure and micro-scale deformation fields.
• To provide direct validation for numerical simulations in materials design.
• To advance the understanding of structure-property relationships in materials.

Main Methods:

• Utilized state-of-the-art 3D tomography techniques.
• Integrated high-resolution deformation field measurements.
• Developed a novel multi-modal dataset correlating microstructure and deformation.

Main Results:

• A one-of-a-kind dataset linking 3D microstructure and deformation fields has been generated.
• The dataset enables detailed analysis of microstructural influences on mechanical response.
• Facilitates validation of computational models for materials design.

Conclusions:

• The presented dataset is essential for developing accurate mechanical property prediction models.
• This data will accelerate the design and optimization of polycrystalline materials.
• Enables deeper insights into the interplay between microstructure and macroscopic mechanical behavior.