251 research outputs found

    Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

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    UID/00667/2020 (UNIDEMI). J. P. Oliveira acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020 Prahalada Rao acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DE-SC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246] for funding his research program. This work espousing the concept of online process monitoring in WAAM was funded through the foregoing DOE Grant (Program Officer: Timothy Fitzsimmons), which partially supported the doctoral graduate work of Mr. Benjamin Bevans at University of Nebraska-Lincoln Benjamin, Aniruddha, and Ziyad Smoqi were further supported by the NSF grants CMMI 1752069 (CAREER) and ECCS 2020246. Detecting flaw formation in metal AM using in-situ sensing and graph theory-based algorithms was a major component of CMMI 1752069 (program office: Kevin Chou). Developing machine learning alogirthms for advanced manufacturing applications was the goal of ECCS 2020246 (Program officer: Donald Wunsch). The XCT work was performed at the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska-Lincoln. The acquisition of the XCT scanner at University of Nebraska was funded through CMMI 1920245 (Program officer: Wendy Crone). Publisher Copyright: © 2022 The AuthorsThe goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.publishersversionpublishe

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Additive Manufactured Antennas and Novel Frequency Selective Sensors

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    The research work carried out and reported in this thesis focuses on the application of additive manufacturing (AM) for the development antennas and novel frequency selective surfaces structures. Various AM techniques such as direct writing (DW), material extrusion, nanoparticle conductive inks are investigated for the fabrication of antennas and FSS based sensors. This research has two parts. The first involves the development of antennas at the microwave and millimetre wave bands using AM techniques. Inkjet printing of nanoparticle silver inks on paper substrate is employed in the fabrication of antennas for an origami robotic bird. This provides an exploration on the practicability of developing foldable antennas which can be integrated on expendable robots using low-cost household inkjet printers. This is followed using Aerosol jet printing in the fabrication of fingernail wearable antennas. The antennas are developed to operate at microwave and millimetre wave bands for potential use in 5G Internet of Things (IoT) or body-centric networks. The second part of the research work involves the development of frequency selective sensors. Trenches have been incorporated on an FSS structure to produce a new concept of liquid sensor. The sensor is fabricated using standard etching techniques and then using FDM method in conjunction with nanoparticle conductive ink. Finally, a new concept displacement sensor using an FSS coupled with a retracting substrate complement is introduced. The displacement sensor is a 3D structure which is conveniently fabricated using AM techniques

    Machine learning for the automation and optimisation of optical coordinate measurement

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    Camera based methods for optical coordinate metrology are growing in popularity due to their non-contact probing technique, fast data acquisition time, high point density and high surface coverage. However, these optical approaches are often highly user dependent, have high dependence on accurate system characterisation, and can be slow in processing the raw data acquired during measurement. Machine learning approaches have the potential to remedy the shortcomings of such optical coordinate measurement systems. The aim of this thesis is to remove dependence on the user entirely by enabling full automation and optimisation of optical coordinate measurements for the first time. A novel software pipeline is proposed, built, and evaluated which will enable automated and optimised measurements to be conducted. No such automated and optimised system for performing optical coordinate measurements currently exists. The pipeline can be roughly summarised as follows: intelligent characterisation -> view planning -> object pose estimation -> automated data acquisition -> optimised reconstruction. Several novel methods were developed in order to enable the embodiment of this pipeline. Chapter 4 presents an intelligent camera characterisation (the process of determining a mathematical model of the optical system) is performed using a hybrid approach wherein an EfficientNet convolutional neural network provides sub-pixel corrections to feature locations provided by the popular OpenCV library. The proposed characterisation scheme is shown to robustly refine the characterisation result as quantified by a 50 % reduction in the mean residual magnitude. The camera characterisation is performed before measurements are performed and the results are fed as an input to the pipeline. Chapter 5 presents a novel genetic optimisation approach is presented to create an imaging strategy, ie. the positions from which data should be captured relative to part’s specific geometry. This approach exploits the computer aided design (CAD) data of a given part, ensuring any measurement is optimal given a specific target geometry. This view planning approach is shown to give reconstructions with closer agreement to tactile coordinate measurement machine (CMM) results from 18 images compared to unoptimised measurements using 60 images. This view planning algorithm assumes the part is perfectly placed in the centre of the measurement volume so is first adjusted for an arbitrary placement of the part before being used for data acquistion. Chapter 6 presents a generative model for the creation of surface texture data is presented, allowing the generation of synthetic butt realistic datasets for the training of statistical models. The surface texture generated by the proposed model is shown to be quantitatively representative of real focus variation microscope measurements. The model developed in this chapter is used to produce large synthetic but realistic datasets for the training of further statistical models. Chapter 7 presents an autonomous background removal approach is proposed which removes superfluous data from images captured during a measurement. Using images processed by this algorithm to reconstruct a 3D measurement of an object is shown to be effective in reducing data processing times and improving measurement results. Use the proposed background removal on images before reconstruction are shown to benefit from up to a 41 % reduction in data processing times, a reduction in superfluous background points of up to 98 %, an increase in point density on the object surface of up to 10 %, and an improved agreement with CMM as measured by both a reduction in outliers and reduction in the standard deviation of point to mesh distances of up to 51 microns. The background removal algorithm is used to both improve the final reconstruction and within stereo pose estimation. Finally, in Chapter 8, two methods (one monocular and one stereo) for establishing the initial pose of the part to be measured relative to the measurement volume are presented. This is an important step to enabling automation as it allows the user to place the object at an arbitrary location in the measurement volume and for the pipeline to adjust the imaging strategy to account for this placement, enabling the optimised view plan to be carried out without the need for special part fixturing. It is shown that the monocular method can locate a part to within an average of 13 mm and the stereo method can locate apart to within an average of 0.44 mm as evaluated on 240 test images. Pose estimation is used to provide a correction to the view plan for an arbitrary part placement without the need for specialised fixturing or fiducial marking. This pipeline enables an inexperienced user to place a part anywhere in the measurement volume of a system and, from the part’s associated CAD data, the system will perform an optimal measurement without the need for any user input. Each new method which was developed as part of this pipeline has been validated against real experimental data from current measurement systems and shown to be effective. In future work given in Section 9.1, a possible hardware integration of the methods developed in this thesis is presented. Although the creation of this hardware is beyond the scope of this thesis

    In-situ monitoring and intermittent controller for adaptive trajectory generation during laser directed energy deposition via powder feeding

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    Laser Directed Energy Deposition (LDED) is one of the advanced manufacturing technologies for building near-net-shaped engineering components in a layer-by-layer fashion using high-power lasers as an energy source. LDED using powder feeding (LDED-PF) is widely used due to its higher dimensional accuracy and ability to build fine features. The quality and performance of LDED-PF-built components are dependent on several factors such as process parameters, process conditions, feedstock properties, system configuration, tool-path generation, etc. Among the above, trajectory control is one of the emerging and active areas of research. Generally, trajectories are developed offline for printing the parts. However, some of the major challenges involved in conventional trajectory development for LDED-PF are the propensity for collision between the deposition head/ nozzle and the part being built and challenges in building components with variable overhang. The major goal of this work is the development of adaptive trajectory control of the LDED-PF process using online and offline techniques to build high-quality components. The work involves the offline trajectory development to build complex-shaped components with variable overhang angles by considering collision between the nozzle head and the part; adaptive layer thickness for higher dimensional accuracy. In addition, the work is extended to the development of online and intermittent trajectory control using a combination of in-situ surface quality monitoring and machine learning technique. Offline trajectory planning is performed for two complex-shaped geometries such as a hemispherical dome and a bent pipe. Offline adaptive trajectory planning for hemispherical dome involves the development of an algorithm including the deposition parameters with variable overhang and collision checking, while the trajectory planning for building bent pipe structures includes the deployment of adaptive slicing in addition to the collision check and overhang angle deposition. To manufacture the dome, the tilt angle is used to avoid the collision between the nozzle and previously built material with a condition that the tilt angle cannot exceed the maximum allowable overhang angle. The algorithm verifies the tilt angle suitable to build the dome and the angle is transferred from the tilt angle to the tilt angle of the rotary table. In order to build the bent pipe geometry, the variation in scanning speed is used to realize the adaptive slicing, which aids in having point-to-point variable layer height thereby permitting non-parallel deposition. In addition, changing the tool orientation during the deposition permits the manufacturing of support-free bent pipe parts as observed for dome structures. LDED-PF of the hemispherical dome and bent pipe was performed using the developed algorithms and the built geometries have good dimensional stability and density. In the case of online trajectory planning, a novel in-situ monitoring software platform was developed for the online surface anomaly detection of LDED-PF parts using machine learning techniques. The above starts with the development of a novel method to calibrate the laser line scanner with respect to the robotic end-effector with sub 0.5 mm accuracy. Subsequently, 2D surface profiles obtained from the LDED-PF built part surface using the laser scanner are stitched together to create an accurate 3D point cloud representation. Further, the point cloud data is processed, and defect detection is carried out using unsupervised learning and supervised (deep) learning techniques. Further, the developed defect detection software platform was used to create an online adaptive toolpath trajectory control platform to correct the dimensional inaccuracies in-situ. It uses a laser line scanner to scan the part after the deposition of the definite number of layers followed by the detection of concave, convex, and flat surfaces using deep learning. Further, the developed adaptive trajectory planning algorithm is deployed by using three different strategies to control material deposition on concave, convex, and flat surfaces. The material deposition is controlled by using adaptive scanning speed, and a combination of laser on-off and scanning speed. Subsequently, the built geometries are subjected to geometric, microstructure, and mechanical characterizations. The study offers an integrated and complete methodology for developing high-quality components using LDED-PF with a minimal dimensional deviation from the original CAD model
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