This project seeks to increase the quality and speed of the inspection of complex metallic parts through the creation of an advisor robotic platform.


This project seeks to build an advisor robotic platform that will provide comprehensive parts inspection of complex metallic components and high-fidelity recommendations for defect identification and characterization. Currently, there is no commercially available reliable advisor robotic inspection system because high reflectivity and the complex geometry of parts hinder accurate characterization of surface profiles. While the project focuses on application in the aerospace market, the results also have the potential to impact automotive, agriculture, and consumer goods markets.


Improve visual and dimensional inspection for complex metallic components, while accelerating speed of inspection and quality.

Technical Approach

Creation of an advisor robot inspection system that is currently not commercially available. The project outputs are also expected to include:

  • Automated robotic data acquisition to map surface quality with multi-facet, multi-sensor measurements
  • Machine intelligence for fault identification
  • Machine learning to continuously improve inspection as more data is collected


University of Washington (Principal Investigator), GKN Aerospace