Date of Award
Spring 2003
Document Type
Thesis - Open Access
Degree Name
Master of Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Yi Zhao
Committee Member
Eric v. K. Hill
Committee Member
Richard P. Anderson
Abstract
Low energy impact damage to a composite structure is difficult to detect and can have profound effects on compressive strengths. Low energy impact damage is sometimes termed as barely visible impact damage (BVID). Detecting BVID is only possible by implementing nondestructive testing (NDT) techniques. Depending upon the support conditions, material system, laminate thickness, lay-up orientation, and impactor geometry, velocity, and hardness, the types of damage associated with BVID include delaminations, longitudinal and transverse matrix cracks, and in some cases, fiber breaks. Material properties such as the strengths of the matrix, fibers, fiber/matrix interface, and more important for BVID, ply interface properties in a multi-ply laminate, are all parameters that determine impact resistance. After the composite structure experiences BVID, the depletion of the structural strength is determined as result of compression after impact (CAI) material testing.
The primary emphasis of this research is to predict structural compressive strength after low energy/low velocity impact using neural networks. After the composite structure absorbs BVID, it is common to determine structural strength depletion based on impact energy. Because impact energy is seldom known in real world applications, it is more reasonable to determine ultimate strength based on amount of damage present. The technique used in this research to assess the damage and predict ultimate strength includes ultrasonic testing (UT), to generate an image representing the damage, and neural networks to predict future performance.
Using the pixel data from the ultrasonic C-scan image of the impact damage, in conjunction with CAI testing, and analyzing it with a backpropagation neural network, correlations on ultimate compressive strength can be made. This analysis demonstrates the ability of a neural network to predict the ultimate compressive strengths of impact damaged composite structures using UT data.
Scholarly Commons Citation
Hess, Christopher D., "Residual Compressive Strength Prediction of Carbon/Epoxy Laminates Subjected to Low Velocity Impact Damage" (2003). Master's Theses - Daytona Beach. 277.
https://commons.erau.edu/db-theses/277
