Deyi Vision Machine Vision Engineering Request RFQ

Selection guide

Machine Vision Acceptance Test Checklist

Define pass/fail samples, repeatability checks, read-rate targets and integration notes before accepting a vision route.

Key takeaways

What this page should help engineering teams decide.

  • Use real good and bad samples.
  • Check repeatability across shifts and surface variation.
  • Acceptance criteria should be measurable before shipment.

Guide to RFQ

Have a real part, sample image or production constraint?

Use the guide to frame the question, then send the details so engineering can recommend a route.

Request engineering RFQ

Guide FAQ

Questions related to machine vision acceptance test checklist.

Ask engineering
How should I use this machine vision acceptance test checklist guide before RFQ?

Use it to collect the application constraints before asking for hardware: target part, defect or measurement, line speed, field of view, working distance, lighting limits and any current reference model.

What information should I send before requesting a machine vision quote?

Send part photos or drawings, target defect or measurement goal, field of view, working distance, line speed, accuracy target, lighting limits and any current camera, lens, light, barcode reader or competitor model.

Do I need a 2D or 3D machine vision system?

Use 2D when contrast, edges, labels or position are enough to judge the part. Use 3D when height, profile, gap, volume, weld shape or surface geometry decides pass or fail.

How should I choose machine vision lighting?

Start from the defect and material surface instead of the camera model. Backlight helps edge measurement, coaxial and dome lighting help reflective surfaces, and bar or ring lighting often works for general presence and defect checks.

Can Deyi Vision support projects that reference Keyence, Cognex or other brand routes?

Deyi Vision is not affiliated with those brands. As a factory-direct machine vision source, we can map the inspection goal, model reference and production route to a component-level camera, lens, light, reader or smart-camera selection.