Line capture plan
Station selection, lighting study, and class balance targets before anyone commits to volume capture.
Hardware Form / AI training data / Manufacturing
AOI · defect vision · line capture
Manufacturing AI training data
Line vision fails quietly. Yield looks fine until a new coating batch or camera vibration opens a blind spot. We build datasets around your stations, optics, and defect taxonomy instead of generic scratch photos.
Manufacturing AI training data collection captures and labels production imagery and video: defects, assemblies, measurements, and trace metadata. We sanitize and balance classes so inspection models generalize across shifts, suppliers, and lighting.
Industrial vision needs planned coverage
SMT, injection molding, final assembly, and packaging each have different failure physics. A model trained on bright uniform boards will miss solder issues under alternate reflow profiles. Dataset specs should name line, SKU, camera model, exposure, and defect codes, not only a raw image count.
| Use case | Label type | Hardware Form role |
|---|---|---|
| Surface defect AOI | Bounding box, segmentation | Line-side capture rigs, rare-defect enrichment |
| Assembly verification | Keypoint / presence | Synced multi-camera setups on your fixture |
| Predictive maintenance | Tabular + vibration spectra | Sensor fusion from industrial programs |
| RAG for SOPs | Document chunks + eval Q&A | Sanitized work instructions with retrieval eval sets |
Defect ontology
Shared taxonomy between quality engineering and annotators: severity, root-cause tags, and reject codes.
Hard-negative mining
Near-miss and cosmetic-variation samples so models do not treat acceptable variation as faults.
Production handoff
Dataset versions mapped to model releases and line software builds for traceability after deploy.
Manufacturing training data FAQ
How many images do we need for defect detection?
You need enough to cover defect types, acceptable variation, and line conditions. There is no universal number. Pilots often start with hundreds per critical class, then scale once false-positive and false-negative rates meet agreed thresholds on a holdout set.
Can you collect on our factory floor?
Yes. We work with your NPI and quality teams, often alongside cameras and edge devices we help specify under direct manufacturing programs.
Do you support edge deployment data loops?
We structure ongoing capture for model refresh when drift is detected, with sanitization before cloud training.