Composite structures are subjected to internal defects and damages such as delamination and voids, rendering visual inspection techniques ineffective. Due to the benefits of non-contact and large area inspection1, active infrared thermography (AIT) is gaining popularity to identify, localize and evaluate sub-surface defects in composite structures. However, images of defects are not always obvious and interpretation of the data by human inspectors varies among individuals, and creates differences in the outcome. Therefore, it is highly desired to develop computerized methods so that consistent analysis of results can be automatically obtained. In this work, convolutional neural networks (CNN) and computer vision were employed to implement two CNN based models for detecting structural defects in samples made of composite materials. The aim is to integrate such deep learning (DL) models to enable interpretation of thermal images automatically. That requires achieving object detection with high enough accuracy so that they can be used to assist human inspectors. The recent success of DL in computer vision tasks such as face recognition among others motivates us to apply DL for boosting the performance of thermal imaging inspections. DL methods were recently evaluated for defect detection in AIT of carbon fiber reinforced plastic (CFRP) composites with handmade defects2. The input for that framework were thermal images acquired during the cooling down process. In our work, we will apply similar concepts to detect and classify void and delamination defects in composites so as to reduce reporting errors and improve consistency.
Carbon fiber reinforced polymer (CFRP) composites are increasingly used in aerospace applications due to its superior mechanical properties and reduced weight. Adhesive bonding is commonly used to join the composite parts since it is capable of joining incompatible or dissimilar components. However, insufficient adhesive or contamination in the adhesive bonds might occur and pose as threats to the integrity of the plane during service. It is thus important to look for suitable nondestructive testing (NDT) techniques to detect and characterize the sub-surface defects within the CFRP composites. Some of the common NDT techniques include ultrasonic techniques and thermography. In this work, we report the use of the abovementioned techniques for improved interpretation of the results.
Laser beam shaping is a widely used technique in many application areas, such as material processing, lithography, optical data storage, and medical procedures. In most cases a laser beam shaping system consists of conventional lenses with curved surfaces. However these lenses are bulky and their fabrication precisions are limited. In this work, we design and fabricate a lens for laser beam shaping using nanostructures. The lens is designed with traditional geometrical optical methods, using energy conservation and optical coordinate transformation algorithms. But instead of using curved surfaces to implement the lens design, we realize the designs with dielectric nanostructures. The lens is then fabricated using electron beam lithography to achieve a high precision. The fabricated lens has very low profile and is capable of fine tuning laser beams. The lens is then experimentally tested. In the experimental setup a laser beam is directed into a multimode fiber and the irradiance of the output beam irradiance profile is measured. Then the lens is placed in front of the multimode fiber and the outcome beam irradiance profile is measured again to test the effects of our laser beam shaping lens.
High power fiber lasers are proposed to be a better candidate than conventional solid-state lasers for industries such
as precision engineering since they are more compact and easier to operate. However, the beam quality generally
degrades when one scales up the output power of the fiber laser.
One can improve the output beam quality by altering the phase of the laser beam at the exit surface, and a promising
method to do so is by integrating specially designed nano-structures at the laser facets. In fact, this method was recently
demonstrated – by integrating gold concentric ring grating structures to the facet of a quantum cascade laser, one
observes significant improvement in the beam quality. Nevertheless, to improve the beam quality of high power fiber
lasers using the method mentioned above, the material of the nano-structures must be able to withstand high laser fluence
in the range of J/cm2.
In this work, we investigated the laser-induced damage threshold (LIDT) values of a suitable material for high
intensity fiber laser applications. Consequently, we demonstrated that the shortlisted material and the fabricated nanostructures
can withstand laser fluence exceeding 1.0 J/cm2.