Autonomous robotic system for thermographic detection of defects in upper layers of carbon fiber reinforced polymers

Carbon Fiber Reinforced Polymers (CFRPs) are composites whose interesting properties, like high strength-to-weight ratio and rigidity, are of interest in many industrial fields. Many defects affecting their production process are due to the wrong distribution of the thermosetting polymer in the upper layers. In this work, they are effectively and efficiently detected by automatically analyzing the thermographic images obtained by Pulsed Phase Thermography (PPT) and comparing them with a defect-free reference. The flash lamp and infrared camera needed by PPT are mounted on an industrial robot so that surfaces of CFRP automotive components, car side blades in our case, can be inspected in a series of static tests. The thermographic image analysis is based on local contrast adjustment via UnSharp Masking (USM) and takes also advantage of the high level of knowledge of the entire system provided by the calibration procedures. This system could replace manual inspection leading to a substantial increase in efficiency

Application of embedded frequency selective surfaces for structural health monitoring

This thesis proposes the use of Frequency Selective Surfaces (FSSs) as an embedded structural health monitoring (SHM) sensor. FSSs are periodic arrays of conductive elements that filter certain frequencies of incident electromagnetic radiation. The behavior of this filter is heavily dependent on the geometry of the FSS and local environment. Therefore, by monitoring how this filtering response changes when the geometric or environmental changes take place, information about those changes may be determined. In previous works, FSS-based sensing has shown promise for sensing normal strain (a stretching or compressing geometrical deformation). This concept is extended in this thesis by investigating the potential of FSSs for sensing shear strain (a twisting deformation) and detection of delamination/disbond (defined as an air gap that develops due a separation between layered dielectrics, and herein referred to as delamination) in layered structures. For normal strain and delamination sensing, monitoring of the FSS\u27s resonant frequency is shown to be a reliable indicator for each phenomena, as verified by full-wave simulation and measurement. For shear strain, simulation results indicate that an FSS may cross-polarize incident radiation when under shear strain. Additionally, FSS was applied as a normal and shear strain sensor within a steel-tube reinforced concrete column, where it was found to provide reliable normal strain detection (as compared to traditional strain sensors), but was not able to detect shear strain. Lastly, in order to improve the design procedure by reducing computation time, an algorithm was developed that rapidly approximates the response of an FSS to delamination through use of conformal mapping and existing frequency response calculations --Abstract, page iii

Two-Dimensional In-Plane Strain Fss-Based Sensor

Frequency selective surface (FSS) based sensors have found application as sensors in the last decade. In this paper, a new sensor design is proposed for two-dimensional in-plane strain sensing. The unit cell of the FSS-based sensor includes two slot dipoles, oriented normal to one another and each with different dimensions, to create two unique resonant frequencies when interrogated with an incident electric field normal to the direction of measured strain. In this way, 2D strain can be characterized concurrently and independently. The error due to strain orthogonal to the direction of interest, along with the error due to the presence of shear strain, has also been characterized. The sensor has a maximum of 12% error for an applied strain due to 4% strain orthogonal to the measurement direction, and no more than 8% error for a maximum of 4% of shear strain

Preliminary Investigations into Selective Laser Melting

Selective laser melting is a promising metallic additive manufacturing process with many potential applications in a variety of industries. Through a gracious donation made by Lawrence Livermore National Laboratory, California Polytechnic State University received and installed an SLM 125 HL selective laser melting machine in February 2017. As part of the initial setup effort, a preliminary machine verification study was conducted to evaluate the general print quality of the machine with default parameter settings. Coincidentally, the as-printed microstructure of SLM components was evaluated through nil strength fracture surface examination, an alternative to conventional polish-and-etch metallography. A diverse set of components were printed on the SLM 125 HL to determine the procedural best practices and inherent constraints. Additionally, the mode and mechanism of failure for a defective Lawrence Livermore National Laboratory component fabricated at their facility was investigated. From these studies, extensive documentation in the form of standard operating procedures, guidelines, templates, and summary reports was generated with the intent of facilitating future selective laser melting research at Cal Poly and strengthening the learning of students interfacing with the novel technology

Ultrasonic nondestructive evaluation of metal additive manufacturing.

Metal Additive Manufacturing (AM) is increasingly being used to make functional components. One of the barriers for AM components to become mainstream is the difficulty to certify them. AM components can have widely different properties based on process parameters. Improving an AM processes requires an understanding of process-structure-property correlations, which can be gathered in-situ and post-process through nondestructive and destructive methods. In this study, two metal AM processes were studied, the first is Ultrasonic Additive Manufacturing (UAM) and the second is Laser Powder Bed Fusion (L-PBF). The typical problems with UAM components are inter-layer and inter-track defects. To improve the UAM process, an in-situ quality evaluation technique was desired. Several NDE techniques were tested in a lab environment before ultrasonic NDE was chosen as a practical, robust, and cost-effective NDE tool. An in-situ monitoring setup was designed and built on an UAM system. NDE results showed interesting features that were simulated through analytic and finite element wave-propagation models. AM layers with defects were characterized as an intact layer and a finite interfacial stiffness spring. The spring stiffness coefficient is a quality parameter that was used to characterize AM layers through a model-based inversion method. In-situ and post-process NDE provided an understanding of defect generation and propagation in UAM. A novel solid-state repair mechanism based on Friction Stir Processing (FSP) was proposed and demonstrated. The quality of L-PBF components depends on several factors including laser power, scan speed, hatch spacing, layer thickness, particle shape/size distribution and other build conditions. Developing process parameters for a new material is an expensive and complex optimization problem. Post-process ultrasonic NDE tests revealed that the model-based in-situ quality monitoring developed for UAM is also applicable to L-PBF Additive Manufacturing. A similar NDE set-up was designed and installed on an open-architecture L-PBF system. A layer-by-layer bond quality evaluation demonstrates the ability to detect good-quality bonds hidden behind poor-quality regions for Inconel 625 alloy. A cost-effective, process parameter development methodology has been proposed and demonstrated