Nondestructive Evaluation (NDE) 4.0 is an emerging approach for providing automation towards material inspection using innovative techniques from Industry 4.0. Such innovative approaches allow for vast data acquisition and analysis potential for physical component assessments that require inspection, or else risk structural failure. Inspection for conductive materials is possible from surface scanning procedures, such as Eddy current testing (ECT). ECT utilizes electromagnetic induction to find defects in conductive materials. In the case of this dissertation, corrosion may be detected with ECT before it continues to grow and damage larger components. Corrosion is “the cancer” of metallics, costing billions in irreversible damages annually. In some instances, corrosion may occur under paints, which may be near invisible through visual inspection. ECT in place may be used, however many components need fast and robust scanning procedures. Fast scanning can be enabled with Eddy current arrays (ECAs), allowing repeated coils that may be used to increase scan areas or cut down scan times, a procedure like a paint brush that obtains information about the material’s health. ECAs also allow for different configurations that may be beneficial for data analysis, such as differential scanning mode. Inspection may be automated using robotic arms systems equipped with ECA, allowing for fast, repeatable, and robust scanning. This may be useful in situations with large components that may be brought into a "robot arm sensor wash" system, such as automobiles or military vehicles. One barrier for enabling robust “freeform” scanning is obtaining the scan path which the ECA will glide along, as components may come in different shapes and sizes, sometimes with curved or complex geometries. The focus of this dissertation is to provide NDE 4.0 techniques along with ECA to detect corrosion along curved steel sheets. NDE 4.0 techniques show capabilities merging cyber-physical systems (CPS), computer vision, and the concept of digital twins between physical and digital space. To enable NDE 4.0 for robotic inspection, a framework was developed, which has five major steps: obtain a reconstruction of the physical object and surrounding environment, orient this virtual scene with respect to the robot’s base frame, generate a toolpath which the NDE probe will be manipulated, conduct the ECA scan with 6-degrees-of-freedom (6-DOF), and process the NDE results. A novel algorithm was developed, “ray-triangle intersection arrays,” which enables pathing on meshes from a raster pattern. The framework used was designed to be generalized for any surface scanning probe, in which ultrasonic testing (UT) scanning for carbon fiber inspection is also demonstrated using the same framework. For ECA, it is important to keep the probe close to the surface while ensuring the distance between the sensor and the probe, or lift-off, is minimized. For the scale of the defects obtained, which is approximately 0.05mm in depth at max, otherwise minor tilts of the probe become significant. The ECA probe contains 32 channels and was operated at 500khz using absolute mode scanning, allowing for exceedingly small defect depths to be detected. The effects of ECA scanning using a robot system are examined, showing that tilt errors from either the path-planning procedure or even the calibration or the robot will provide significant errors. To better understand the effects per coil, a “full” scan mode was examined, showing a larger image per coil, as well as the typical painting scan considered as a “fast” scan. Other errors such as heating were also examined. With knowledge of the errors from robotic scanning, post-processing procedures were developed to minimize errors. A novel algorithm “array subtraction” was developed to reduce lift-off from common factors seen in every coil, indicating prob tilt error. A digital microscope was used to compare defects ground-truth defect volume with the ECA results, in which defect versus background intersection masking was used. Three objectives are discussed to cover the generalized robust surface scanning framework, the dissection of effects of robotic scanning for ECA for corroded surfaces, and how to process and interpret this ECA data. The results show promising future applications for robust surface scanning as corrosion is decently detected. Future applications would be the previously mentioned carwash system, AI-enabled detection, and mobile platforms to expand on inspection workspaces.
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