Advanced Materials, Characterization and Non-Destructive Evaluation

Overview and Rationale The rapid evolution of advanced materials and manufacturing technologies has created a pressing need for deeper understanding, accurate characterization, and reliable evaluation of structural integrity. Emerging classes of materials such as bulk metallic glasses, high-entropy alloys, nickel-based superalloys, nanostructured materials, composites, and functional metamaterials are increasingly employed across aerospace, automotive, energy, biomedical, and defense sectors. Their performance and reliability depend not only on intrinsic material design but also on robust characterization techniques and non-destructive evaluation (NDE) methods.
Simultaneously, the integration of machine learning, digital twins, and data-driven approaches is reshaping how materials are designed, tested, and monitored. These developments call for an interdisciplinary platform that unites materials innovation, advanced testing, and modern evaluation techniques.

Aims and Scope
This Research Topic aims to advance the science and engineering of materials characterization and NDE by bringing together contributions that:

o Develop and process new and advanced materials (metallic, ceramic, polymeric, composite, glassy, metamaterials).
o Provide microstructural and mechanical characterization across multiple length scales, including in-situ and operando methods.
o Advance non-destructive testing/evaluation (NDT/NDE) techniques such as ultrasonics, thermography, radiography, acoustic emission, eddy current, and hybrid/multi-modal approaches.
o Apply artificial intelligence, machine learning, and computational tools to materials design, data fusion, inversion, and structural health monitoring (SHM).
o Address reliability, damage detection, delamination, defect evolution, and lifetime prediction in materials and components.
o Bridge materials development, testing methodologies, and reliability assessment for improved structural performance and safety.

The rapid introduction of new and complex materials has outpaced standardized characterization and evaluation methodologies. Traditional destructive testing, while accurate, is often costly, time-consuming, and impractical for in-service components. Advanced materials also exhibit heterogeneous microstructures, anisotropy, hidden defects, and complex damage mechanisms (e.g., delamination, microcracking) that challenge conventional detection and prediction strategies.

This Research Topic addresses these challenges by promoting research that:

o Ensures material integrity and long-term reliability through robust, scalable testing frameworks.
o Develops advanced, non-destructive techniques for defect detection, condition assessment, and health monitoring.
o Leverages digital technologies, AI, and machine learning to process large experimental datasets, enable data-driven modeling, and enhance predictive capability.
o Integrates materials innovation with NDE and SHM for safer, more efficient engineering applications.

Topics of Interest

o Materials: bulk metallic glasses, high-entropy alloys, nickel-based superalloys, nanostructured and functional materials, composites, metamaterials.
o Manufacturing: additive manufacturing, advanced processing routes, process–structure–property relationships.
o Characterization: microscopy, spectroscopy, diffraction, tomography, in-situ/operando testing, multi-scale and multi-physics approaches.
o NDT/NDE: ultrasonics (bulk and guided waves), thermography, radiography/CT, acoustic emission, eddy current, laser ultrasonics, hybrid/multi-sensor methods, data fusion.
o SHM and Reliability: damage initiation and evolution, delamination, defect quantification, prognosis, lifetime prediction under complex loading and environments.
o Data and Computation: machine learning, inverse methods, digital twins, uncertainty quantification, physics-informed learning, surrogate modeling for design and diagnosis.
o Interdisciplinary studies linking materials innovation, testing methodologies, and reliability assessment.

Article Types
o Original Research
o Review Articles
o Mini Reviews / Short Communications
o Case Studies / Technical Notes
o Methods and Data Reports (including benchmark datasets or open protocols)

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Advanced materials, NDT&E, Material Characterization, Metamaterials, High Resolution imaging, Ultrasonics, Machine learning applications for advanced materials and optimization

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.