Frequently asked questions.

Waywiser systems detect surface defects — scratches, dents, cracks, contamination, coating irregularities — as well as dimensional deviations from tolerance. The specific detection scope depends on the inspection modality (2D surface, 3D dimensional, or combined) and the sensor configuration chosen for your application.

Waywiser systems are designed for production-speed inspection. Actual cycle time depends on the part geometry, inspection scope, and sensor configuration. Systems are engineered to match your production throughput requirements without becoming a bottleneck.

The 2D system detects visible surface deviations — scratches, contamination, coating defects, and visual anomalies. The 3D system performs dimensional verification against defined tolerances — lengths, diameters, thread geometry, and form deviations. Many deployments combine both modalities in a single inspection cycle, depending on customer requirements.

Yes. The inspection software supports combined 2D and 3D inspection within the same station. Surface inspection and dimensional measurement run in one cycle, producing a single production decision per part — documented with both surface heatmap and dimensional results.

Yes. Waywiser systems are available as compact stand-alone cells for independent operation, or as fully integrated inspection stations embedded into automated production lines. Integration scope — feeding concepts, sorting, line control, MES/ERP connectivity — is adapted to your production environment.

The software supports data export to MES, ERP, and quality management systems. Inspection results, images, heatmaps, and measurement data can be exported via CSV, database connection, or direct system integration — configured for your traceability and compliance requirements.

Deployment timelines depend on integration complexity. The reference-part-based teach-in simplifies commissioning significantly — the system learns from one good part, with no defect libraries or manual feature programming required. Stand-alone cells commission faster; fully integrated line deployments include additional validation and process adaptation.

One good reference part. The system learns what a correct part looks like and flags deviations from that learned reference. No defect samples, no annotated image datasets, and no manual feature programming are required to begin inspection.

The AI learns the appearance and geometry of your reference part during automatic teach-in. During production, every part is compared against that learned reference. Deviations are detected, visualized on a heatmap, and classified as OK or NOK — with the decision and evidence stored for traceability.

Yes. Training runs locally on the inspection hardware. The system is designed to operate self-contained — no external compute infrastructure is required for standard deployments.

Every inspected part produces a heatmap overlay showing exactly where and how the part deviates from the learned reference. This makes every AI decision transparent, verifiable, and auditable by quality engineers.

The system's ability to handle orientation variation depends on the handling concept and inspection setup. Compact cells with controlled feeding deliver consistent orientation. For less controlled environments, the inspection configuration is adapted to accommodate expected variation.

Yes. Each part type is stored as a recipe. Changeover between part types is fast — select the recipe, and the system is ready for the next production run. This supports mixed production and frequent part changes without lengthy reconfiguration.