The US Air Force Research Laboratory's (AFRL) Materials and Manufacturing Directorate and Intelligent Automation have together developed a new tool to speed up routine aircraft engine exhaust inspections.

The Auto-Scan inlet and exhaust damage registration sensor was developed as part of a $4m Critical Small Business Innovative Research contract with Intelligent Automation.

Using the damage registration sensor, personnel responsible for the maintenance of high-performance aircraft can quickly characterise inlet and exhaust damage in the aircraft.

The sensor electronically captures an image of the surface defect and it determines shape, outlines the perimeter, and calculates area, depth, location, and orientation of the defect.

The information gathered is then transferred to the aircraft’s health assessment system for further analysis, AFRL said in a statement.

AFRL project engineer Juan Calzada said: “This tool changes the face of routine field maintenance by making it not only more accurate, but faster and easier, so that we decrease the amount of time the aircraft are out of service.

“It also ensures data is recorded easily and accurately, so it really makes the maintainer’s job a lot easier all around.”

The aircraft need regular inspections and maintenance as they routinely experience high-temperatures and extreme operating conditions.

"This tool changes the face of routine field maintenance by making it not only more accurate, but faster and easier."

The current inspections include visual analysis and manual trace and transfer plotting of damage occurrences.

The existing process usually involves manual methods, therefore increasing chances of inaccurate or incomplete interpretation and human error.

Following the successful demonstration of the Auto-Scan tool, AFRL is now transitioning it for operational deployment to the field as part of a $2.5m Rapid Innovation Fund effort.


Image: An aircraft maintainer demonstrates the Auto-Scan inlet and exhaust damage registration sensor. Photo: courtesy of Intelligent Automation, Inc.