Authors
Christopher Sacco, Anis Baz Radwan, Tyler Beatty, Ramy Harik
Conference
SAMPE Conference & Exhibition. Charlotte, North Carolina, US.
Abstract
Automated Fiber Placement (AFP) has become a standard manufacturing technique in the creation of large scale composite structures due to its high production rates. However, the associated rapid layup that accompanies AFP manufacturing has a tendency to induce defects. We forward an inspection system that utilizes machine learning (ML) algorithms to locate and characterize defects from profilometry scans coupled with a data storage system and a user interface (UI) that allows for informed manufacturing. A Keyence LJ-7080 blue light profilometer is used for fast 2D height profiling. After scans are collected, they are process by ML algorithms, displayed to an operator through the UI, and stored in a database. The overall goal of the inspection system is to add an additional tool for AFP manufacturing. Traditional AFP inspection is done manually adding to manufacturing time and being subject to inspector errors or fatigue. For large parts, the inspection process can be cumbersome. The proposed inspection system has the capability of accelerating this process while still keeping a human inspector integrated and in control. This allows for the rapid capability of the automated inspection software and the robustness of a human checking for defects that the system either missed or misclassified.
Citation
Christopher Sacco, Anis Baz Radwan, Tyler Beatty, & Ramy Harik. (May 2019). Machine Learning Based AFP Inspection: A Tool for Characterization and Integration. SAMPE Conference & Exhibition. Charlotte, North Carolina, US. doi:https://ntrs.nasa.gov/search.jsp?R=20200002684
Link: https://ntrs.nasa.gov/search.jsp?R=20200002684
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