Value Proposition:
· Non-Destructive 3D Characterization: The technology enables real-time, non-invasive monitoring of defect evolution in crystalline materials, providing high-resolution insights without altering or damaging the sample.
· Spatio-Temporal Resolution: Captures defect dynamics at nanometer-scale spatial resolution and nanosecond-scale temporal resolution, surpassing conventional imaging techniques.
· Advanced Computational Reconstruction: Uses machine learning and physics-based modeling to process surface measurements and reconstruct 3D defect structures within bulk materials.
· Versatile Applications: Applicable in materials research, alloy development, manufacturing quality control, and predictive modeling for material failure and fatigue life.
· Bridging Surface Observations to Bulk Behavior: Integrates elastodynamic displacement measurements with computational algorithms to reveal defect interactions at multiple scales.
Unmet Need:
The study of defect dynamics is crucial in materials science, as defects significantly influence mechanical, thermal, electrical, and other material properties. Current methods face challenges like limited 3D characterization, inadequate temporal resolution, or the need for destructive testing. High costs and accessibility issues also hinder advanced imaging techniques. This new technology overcomes these limitations by providing high-resolution, non-destructive, real-time 3D characterization of defect dynamics, which could benefit materials science research, development and optimization of new alloys, and predictive modeling of material failure and fatigue life.
Technology Description:
Researchers at Johns Hopkins University have developed a cutting-edge system for 3D Material Defect Evolution Reconstruction, integrating advanced sensing, signal processing, and visualization techniques to provide real-time, non-destructive monitoring of defect behavior. The system utilizes acoustic emission (AE) sensors to detect elastic waves generated by defect movement or laser interferometry to measure surface displacements with nanometer-scale precision. Advanced signal processing and machine learning algorithms interpret raw measurement data to provide real-time in-situ monitoring of subsurface defect evolution.
Stage of Development:
Proof-of-Concept Completed:
- A 3D reconstruction algorithm has been developed to interpret high-frequency displacement measurements.
- Computational validation has been performed using synthetic datasets generated from discrete dislocation elastodynamics simulations.
Data Availability:
· Data available upon request.
Select Publications:
Junjie Yang, Daniel Magagnosc, Tamer A Zaki, Jaafar A El-Awady, "The three-dimensional elastodynamic solution for dislocation plasticity and its implementation in discrete dislocation dynamics simulations." Acta Materialia 253, 118945 (2023).
Junjie Yang, Daniel Magagnosc, Jaafar A El-Awady, Tamer A Zaki, Reconstructing dislocation slip evolution by assimilation of elastodynamic displacement signatures, Acta Materialia 284, 120627 (2025)
