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.
This video showcases how AI-powered quality assurance systems monitor titanium WAAM builds in real-time, analyzing thermal gradients, layer adhesion, and microstructure formation to predict and prevent defects before they occur. Learn how automated quality control reduces scrap rates, accelerates certification, and enables consistent production of aerospace-grade titanium components.
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™ and VisiCore™ AI systems for multi-modal WAAM quality monitoring.
- Integrate with QMS Copilot 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.
Transcript (short)
Show transcript
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.