AI in Materials Engineering
Been working on a side project combining metallurgy + computer vision/MLOps.
The idea is basically to speed up metallographic characterization workflows using modular CV pipelines instead of fully manual inspection/reporting.
Current framework structure:
- Semantic segmentation for phase quantification in low-alloy carbon steel microstructures
- Object detection layer for cracks, macro-porosity, and coarse precipitates
- Middleware that converts feature maps into engineering metrics (area fractions, defect boundaries, etc.)
- Automated PDF inspection reporting with overlays + summarized outputs
One thing I realized quickly is that data availability is probably the biggest bottleneck in this space. Getting high-quality pixel-level annotated micrographs is extremely difficult unless you already have access to industrial archives or institutional datasets.
So instead of waiting for “perfect data,” I focused heavily on the software + deployment side first.
One interesting engineering problem was handling model evolution during deployment. I built a compatibility layer that inspects model state_dicts and remaps incompatible layer keys dynamically so experimental architecture changes don’t completely break deployment pipelines or require retraining from scratch every time.
Still very much an MVP.
The biggest challenge right now is that CV models are good at statistical feature grouping, but they obviously don’t understand thermodynamics or transformation kinetics. Things like:
- varying etchants
- magnification changes
- bainite vs tempered martensite morphology
- inconsistent polishing quality
…still create edge cases that are difficult to generalize.
Long-term, I’d like to explore physics-informed training constraints so predictions remain metallurgically consistent instead of purely visual/statistical.
The goal isn’t replacing metallurgists — more reducing repetitive feature counting and inspection reporting time.
Would genuinely love feedback from:
- people working in computational metallurgy
- QA/steel manufacturing
- physics-informed ML
- anyone who has worked with micrograph datasets
Also curious if anyone here has seen similar work being used successfully in industry already.