In-situ detection of stochastic spatter-driven lack of fusion: Application of optical tomography and validation via ex-situ X-ray computed tomography

The presence of random defects in laser powder bed fusion (LPBF) parts is an issue that challenges the reliability of this manufacturing process and hinders its employment in structural, defect-sensitive components. A potential solution to increase the reliability of LPBF is employing in-process monitoring targeting defect detection. This study aims to detect stochastic defects driven by spatter particles via in-situ monitoring and validate the detection method ex-situ via X-ray computed tomography (XCT). By means of in-situ optical tomography (OT), monitoring images were registered layerwise during the manufacturing of Hastelloy X specimens. The images were analyzed to detect spatters landing within specimen boundaries, and the spatial coordinates of the detections were obtained. The specimens were also measured ex-situ by means of XCT, from which key features and coordinates of internal defects were obtained. The in-situ spatter detection method was then compared to the XCT measurements. It was found that 79 % of lack of fusion defects were detected in OT images. The detection was particularly successful for large defects. Spatter-induced lack of fusion defects were present in the specimens manufactured with optimized processing parameters in different degrees, depending on the robustness of the processing conditions to spatters. This study demonstrates the applicability of optical tomography in-situ monitoring for indirect detection of stochastic lack of fusion, whose presence is inferred from spatter redeposits on the powder bed

Segmentation of Photovoltaic Module Cells in Electroluminescence Images

High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an $F_1$ score of 97.54%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

Computer aided detection of defects in FRP bridge decks using infrared thermography

The objective of this research is to develop a turn-key system that is able to interface with the FLIR ThermaCAM S60 infrared camera and automatically capture and analyze defects in infrared images of FRP bridge decks. Infrared thermography is one of the nondestructive evaluation (NDE) techniques that are being used to locate defects (debonds and delaminations) in bridge components. It is a rapid data collection and interpretation technique having high sensitivity and reliability. Analysis of infrared images by human interpretation is dependent on the users knowledge and hence introduces ambiguity in the defect detection process.;This thesis investigates the use of an automated defect detection system to locate defects in infrared images of FRP bridge decks to eliminate/reduce human intervention. Air-filled and water-filled debonds were inserted between the wearing surface and the underlying FRP deck. Also, simulated subsurface delaminations (of various sizes and thickness) were created at the flange-to-flange junction between two FRP deck modules. (Abstract shortened by UMI.)

Towards the Fabrication Strategies for Intelligent Wire Arc Additive Manufacturing of Wire Structures from CAD Input to Finished Product

With the increasing demand for freedom of part design in the industry, additive manufacturing (AM) has become a vital fabrication process for manufacturing metallic workpieces with high geometrical complexity. Among all metal additive manufacturing technologies, wire arc additive manufacturing (WAAM), which uses gas metal arc welding (GMAW), is gaining popularity for rapid prototyping of sizeable metallic workpieces due to its high deposition rate, low processing conditions limit, and environmental friendliness. In recent years, WAAM has been developed synergistically with industrial robotic systems or CNC machining centers, enabling multi-axis free-form deposition in 3D space. On this basis, the current research of WAAM has gradually focused on fabricating strut-based wire structures to enhance its capability of producing low-fidelity workpieces with high spatial complexity. As a typical wire structure, the large-size free-form lattice structure, featuring lightweight, superior energy absorption, and a high strength-weight ratio, has received extensive attention in developing its WAAM fabrication process. However, there is currently no sophisticated WAAM system commercially available in the industry to implement an automated fabrication process of wire or lattice structures. The challenges faced in depositing wire structures include the lack of methods to effectively identify individual struts in wire structures, 3D slicing algorithms for the whole wire structures, and path planning algorithms to establish reasonable deposition paths for these generated discrete sliced layers. Moreover, the welded area of the struts within the wire structure is relatively small, so the strut forming is more sensitive and more easily affected by the interlayer temperature. Therefore, the control and prediction of strut formation during the fabricating process is still another industry challenge. Simultaneously, there is also an urgent need to improve the processing efficiency of these structures while ensuring the reliability of their forming result

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Nanoscale Optical and Correlative Microscopies for Quantitative Characterization of DNA Nanostructures

Methods to engineer nanomaterials and devices with uniquely tailored properties are highly sought after in fields such as manufacturing, medicine, energy, and the environment. The macromolecule deoxyribonucleic acid (DNA) enables programmable self-assembly of nanostructures with near arbitrary shape and size and with unprecedented precision and accuracy. Additionally, DNA can be chemically modified to attach molecules and nanoparticles, providing a means to organize active materials into devices with unique or enhanced properties. One particularly powerful form of DNA-based self-assembly, DNA origami, provides robust structures with the potential for nanometer-scale resolution of addressable sites. DNA origami are assembled from one large DNA scaffold strand and many unique, short staple strands; each staple programmatically binds the scaffold at several distant domains, and the coordinated interactions of many staples with the scaffold act to fold the scaffold into a desired shape. The utility of DNA origami has been demonstrated through multiple applications, such as plasmonic and photonic devices, electronic device patterning, information storage, drug delivery, and biosensors. Despite the promise of DNA nanotechnology, few products have successfully translated from the laboratory to industry. Achieving high yield and high-precision synthesis of stable DNA nanostructures is one of the biggest challenges to applications of DNA nanostructures. For adoption in manufacturing, methods to measure and inspect assembled structures (i.e. metrology) are essential. Common high-resolution imaging techniques used to characterize DNA nanostructures, such as atomic force microscopy and transmission electron microscopy, cannot facilitate high-throughput characterization, and few studies have been directed towards the development of improved methods for nanoscale metrology. DNA-PAINT super-resolution microscopy enables high-resolution, multiplexed imaging of reactive sites on DNA nanostructures and offers the potential for inline optical metrology. In this work, nanoscale metrologies utilizing DNA-PAINT were developed for DNA nanostructures and applied to characterize DNA origami arrays and single site defects on DNA origami. For metrology of DNA origami arrays, an embedded, multiplexed optical super-resolution methodology was developed to characterize the periodic structure and defects of two-dimensional arrays. Images revealed the spatial arrangement of structures within the arrays, internal array defects, and grain boundaries between arrays, enabling the reconstruction of arrays from the images. The nature of the imaging technique is also highly compatible with statistical methods, enabling rapid statistical analysis of synthesis conditions. To obtain a greater understanding of DNA origami defects at the scale of individual strands, correlative super-resolution and atomic force microscopies were enabled through the development of a simple and flexible method to bind DNA origami directly to cover glass, simultaneously passivating the surface to single-stranded DNA. High-resolution, correlative microscopy was performed to characterize DNA origami, and spatial correlation in super-resolution optical and topographic images of 5 nm was achieved, validating correlative microscopy for single strand defect metrology. Investigations of single strand defects showed little correlation to structural defects on DNA origami, revealing that most site defects occur on strands that are present in the structure, contrary to prior reports. In addition, the results suggest that the structural stability of DNA origami was decreased by DNA-PAINT imaging. The presented work demonstrated the development and application of advanced characterization techniques for DNA nanostructures, which will accelerate fundamental research and applications of DNA nanotechnology

Monitoring the Metal Additive Manufacturing Process through Thermographic Data Analysis

Metal Additive Manufacturing (AM) is the formation of a solid metal part through the layer-wise melting of metal powder, wire, or thin sheets using various heat sources or other bonding methods. This manufacturing method provides nearly limitless complexity with decreased waste, energy needs, and lead time. However, the process faces challenges in part consistency and validation especially for high precision fields such as aerospace and defense. Research has sought to implement robust process monitoring techniques to increase consistency and the reliability of the AM process and detect vital information about the part such as microstructural development, and porosity formation so that eventually the proper control systems can be created to control the desired outcome. The research performed in this thesis seeks to utilize one of the more promising monitoring techniques for the metal PBF processes (selective laser sintering and electron beam melting), infrared (IR) thermography. However, little research has been performed using the technology, and therefore few monitoring applications for AM have been developed. The methods and results in this research will show two potential applications for the use of IR thermography to monitor AM materials: microstructural monitoring and porosity detection. The research will also discuss an algorithmic method for calibrating IR signals for in-situ emissivity change of the material to obtain a more accurate temperature history of a part during the build and direction for future work that needs to be addressed to advance the technology further