AI and Metallography – From Fundamental Research to Industrial Routine and Beyond

Quantitative microstructure analysis is undergoing a profound transformation. Modern image processing and machine learning approaches offer capabilities that substantially surpass traditional manual methods in terms of speed, reproducibility, and statistical significance. Yet industrial practice consistently demonstrates that the path from scientific innovation to reliable routine application is accompanied by considerable challenges.

This presentation traces the evolution from early algorithmic approaches to today's AI-assisted systems for grain size, phase, layer, and inclusion analyses. Central to the discussion is a fundamental question: How do academic proof-of-concept studies become robust, validated, and standards-compliant tools for industrial materials testing? Critical aspects addressed include training data quality, ground truth definition, result traceability, standards-conformant validation, and integration into established laboratory workflows.

The talk also opens a perspective on future developments, including multimodal data fusion and correlative microscopy, self-learning systems, and the integrative linkage of microstructure with process parameters and material properties. It explores how AI may move beyond pure microstructure description by identifying complex structure–property–process relationships, enabling predictive capabilities, and actively supporting materials development.

A key message of this presentation: AI does not replace the expertise of metallographers and materials testing professionals – quite the contrary. Their domain knowledge becomes more indispensable than ever– for the informed interpretation of training data, critical validation of results, assessment of physical plausibility, and the responsible integration of AI-derived insights into industrial decision-making. The future of metallography therefore lies not in automation as a substitute for domain expertise, but in the productive interplay between materials science competence and data-driven analytics.