Date of Award
Summer 2019
Document Type
Open Access Thesis
Department
Computer Science and Engineering
First Advisor
Ramy Harik
Abstract
The advanced manufacturing capabilities provided through the automated fiber placement (AFP) system has allowed for faster layup time and more consistent production across a number of different geometries. This contributes to the modern production of large composite structures and the widespread adaptation of composites in industry in general and aerospace in particular. However, the automation introduced in this process increases the difficulty of quality assurance efforts and inspection. The AFP process can induce a number of manufacturing defects including wrinkles, twists, gaps, and overlaps. The manual identification of these defects is often laborious and requires a measure of expert knowledge. A software package for the assistance of the inspection process has been used in conjunction with automated inspection hardware for the automated inspection, identification, and characterization of AFP manufacturing defects. Image analysis algorithms were developed and demonstrated on a number of defect types. Defects are identified in scan images and exact size and shape characteristics are extracted for export.
Rights
© 2019, Christopher Sacco
Recommended Citation
Sacco, C.(2019). Machine Learning Methods for Rapid Inspection of Automated Fiber Placement Manufactured Composite Structures. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/5475