RoboFacetRoboFacet

Visual Inspection & Quality

Use machine vision, drones and robots to find defects and verify production quality.

Clear

Questions to answer before choosing

Use these three questions to define the job before comparing products.

  • Which defect classes matter and how many labeled examples are available?
  • What false-reject and missed-defect rates can the process tolerate?
  • Must results be traceable to a batch, serial number or production station?

When not to automate yet

Consider whether improving the current workflow would meet your needs first.

Works well when
  • Defect classes that are named, agreed and illustrated with examples
  • Enough labelled history to test against real parts, not demos
  • Results that can be traced back to a batch, station or serial number
What to check
  • Missed-defect and false-reject tolerance separately — they cost different things
  • How the system is re-validated after a lens, light or material change
  • Who reviews a part the system rejected
Not a magic solution
  • A replacement for a written defect standard
  • A system that absorbs a new defect class it has never seen
  • A reason to skip sampling checks after go-live

Three ways to solve the same problem

Choose the lowest layer that reliably removes the bottleneck.

01

Process first

Standardize the workflow, remove unnecessary steps and measure the bottleneck before buying technology.

02

Software automation

Use workflows, RPA or AI agents when the work is digital and the inputs and exceptions can be defined.

03

Physical robot

Use an arm, AMR, drone or humanoid when movement in the real world is the constraint to remove.

Tools for this scene · 178 tools

Also relevant · 10 tools

These products are useful in adjacent workflows, but the fit is less direct. Open them when your project spans more than one task.