Scanning Acoustic Microscopy (SAM) is a powerful tool for non-destructive evaluation of internal features such as voids, delamination, and cracks but traditional systems have limitations in scale, geometry, and data interpretation.
In this webinar, Covalent and PVA TePla will showcase how new advances in large-format scanning, rotational inspection, and machine learning are overcoming these challenges enabling faster, more consistent, and more scalable defect detection.
What you’ll learn
- Inspect large components with full-field 1 m × 1 m scanning
- Analyze cylindrical and disc geometries with rotational scanning
- Improve defect detection using ML applied directly to A-scan waveforms
- Reduce operator variability and increase inspection consistency
What’s new & why it matters
Covalent’s PVA TePla Okos MacroVue system enables inspection of larger and more complex components than traditional SAM tools. Paired with machine learning–driven signal recognition, this approach delivers faster analysis, improved sensitivity to subtle defects, and more consistent results in high-throughput environments.
Real-world applications
- Sub-surface crack detection in chamber components
- Coating thickness measurement using time-of-flight
- In-line QC for cylindrical battery cells
- ML-based differentiation of voids vs thickness variation
Director of MCS Operations, Covalent
Associate Director of Support & Applications, PVA TePla OKOS
