Inspecting part quality is currently a slow, manual process that lacks real-time data metrics, allowing for "process-drift". This automated solution seeks to minimize process drift and add real-time data metrics.


This project seeks to improve the processes surrounding part quality inspection through the development of an easily programmable robotics 3D inspection system. Success will be measured through improvements in resolution, cycle time, programming time, and enhanced insights. By project end, the team intends to demonstrate an automatically calibratable system that will work with existing CMMs.


Automate the qualification process by developing a robotic 3D inspection system that can be easily programmed (off-line or by lead-thru teaching) using industrial PFL cobots and an AI engine to collect high-resolution 3D scan data to resolve quality backlog issues and process drift early on.

Technical Approach

The expected project outputs are:

  • Algorithms that improve human-robot collaboration
  • Robotic 3D scanning software tools
  • Workforce training materials
  • Masked data


Fiat Chrysler (PI), Aris Technology, Yaskawa