This project greatly increased the quality and speed of the inspection of complex metallic parts through the creation of an advisor robotic platform.

The ARM-Funded technology Project “Automated Defect Inspection for Complex Metallic Parts” has yielded impressive results, including detection rates above 95%, a speed of approximately one minute per part, and an expected 345% return on investment if deployed at one site. GKN Aerospace is adopting the technology, and has already built the developed inspection cell in a factory.

Project

This project built 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.

Objective

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

Technical Approach

The team’s central innovation is that fusing noncontact imaging data – robotically collected at the right positions and in the right environment – enables high-quality, consistent surface profiling at required operation speeds. From there, machine-learning based data analytics continuously improves inspection performance using accumulated data. Read more here. 

Participants

University of Washington (Principal Investigator), GKN Aerospace