Date of Award

12-2019

Degree Name

MS in Mechanical Engineering

Department

Mechanical Engineering

College

College of Engineering

Advisor

Dr. Eltahry Elghandour

Advisor Department

Mechanical Engineering

Advisor College

College of Engineering

Abstract

Composite materials are extensively used as an advanced engineering material, particularly in aerospace, automotive, and buildings industries due to its superior properties such as high strength to weight ratios and resistance to corrosion. As composite materials are rapidly replacing traditional materials in aircraft manufacturing, improved methods of identifying damage and critical failure is in development. One of the most commonly used procedures utilizes a health monitoring system that relies on transducers to monitor transmitted waves generated by ultrasonics. By replacing this method with a nanotechnology-based one, it is possible to efficiently detect damage without the time-extensive process of scanning the structure. This research investigated the development of a nanomaterial-based sensor for health monitoring of composite structures. To develop the sensor, carbon nanotube/epoxy mixture (2%wt CNT) was coated on a strand of E-glass fibre to be adhered onto a fiberglass composite specimen. The selection of E-glass fibre and fibreglass plate was largely due to its electrical insulating properties to demonstrate that the carbon nanotube is driving the sensing capabilities through its highly conductive nature. In addition, by adhering the coated E-glass fiber to a fibreglass coupon, the homogeneity and material properties were approximately maintained. Tensile testing of the specimen conducted through a Lloyd LD50 tensile testing machine provided data on the actual strain which was correlated with the experimental differential resistances measured by a multimeter, both at the same specified tensile loading conditions. With two sets of data, the experimental resistance data was calibrated with the actual strain data collected. Ultimately, the experimental sensors created a sample of gauge factors which represents 91.24% probability of replicating the observed range of gauge factors by using the same manufacturing procedures, providing a valid alternative and consistent method to detecting composite damage.

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