AI-Powered Quality Assurance for Titanium WAAM
Discover how AI-driven quality assurance systems transform titanium WAAM production, ensuring every layer meets aerospace standards through automated monitoring and predictive analytics.
Key Takeaways
- Automated quality assurance through AI analysis of thermal and visual data streams.
- Predictive defect detection that identifies issues before they impact build quality.
- Layer-by-layer quality scoring with real-time pass/fail criteria for aerospace standards.
- Comprehensive traceability and documentation for certification and audit requirements.
Deploy with Therness
- Deploy HeatCore™ thermal and PoolDrop high-speed visual systems for multi-modal WAAM quality monitoring.
- Integrate with your QMS for automated CAPA workflows and compliance reporting.
- Train custom AI models on your specific titanium alloys and build geometries.
- Scale quality assurance across production lines with centralized model management.
How layer-by-layer scoring works
Quality assurance in WAAM is really a question of catching a bad layer before the next one buries it. Each deposited bead has a thermal history — a peak temperature, a cooling curve, and an interpass temperature the following pass starts from — and that history is what decides the local microstructure, grain size, and residual stress in the finished wall. The system fingerprints every layer against the learned envelope for the alloy: a cooling curve that flattens signals heat piling up and a coarsening grain structure; a cold spot or a shielding lapse flags porosity and lack-of-fusion risk. Instead of a pass/fail verdict at the end of a multi-hour build, you get a running score tied to a specific layer and coordinate, so a drift can be corrected in place. The upside is fewer scrapped near-net-shape parts and a complete, traceable record that maps directly onto aerospace additive-manufacturing acceptance requirements.
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Transcript (short)
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The video shows AI-powered quality assurance for titanium WAAM, analyzing layer-by-layer signals to predict defects before they lock into the build. It highlights real-time scoring against acceptance criteria, traceability of results, and how teams can reduce scrap while accelerating aerospace certification workflows.
