461 research outputs found

    3D printing of oil paintings based on material jetting and its reduction of staircase effect

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    Material jetting is a high-precision and fast 3D printing technique for color 3D objects reproduction, but it also suffers from color accuracy and jagged issues. The UV inks jetting processes based on the polymer jetting principle have been studied from printing materials regarding the parameters in the default layer order, which is prone to staircase effects. In this work, utilizing the Mimaki UV inks jetting system with a variable layer thickness, a new framework to print a photogrammetry-based oil painting 3D model has been proposed with the tunable coloring layer sequence to improve the jagged challenge between adjacent layers. Based on contour tracking, a height-rendering image of the oil painting model is generated, which is further segmented and pasted to the corresponding slicing layers to control the overall printing sequence of coloring layers and white layers. The final results show that photogrammetric models of oil paintings can be printed vividly by UV-curable color polymers, and that the proposed reverse-sequence printing method can significantly improve the staircase effect based on visual assessment and color difference. Finally, the case of polymer-based oil painting 3D printing provides new insights for optimizing color 3D printing processes based on other substrates and print accuracy to improve the corresponding staircase effect

    Supportless Fabrication, Experimental, and Numerical Analysis of the Physical Properties for a Thin-Walled Hemisphere

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    Although multi-axis bead deposition-based additive manufacturing processes have been investigated in many aspects in the literature, a general process planning approach to address collision detection and prevention still needs to be developed to fabricate complex thin-wall geometries in a supportless fashion. In this research, an algorithm is presented that partitions the surfaces of the part and finds the appropriate tool orientation for each partition to avoid collisions. This algorithm is applied to segment the surface of a thin-wall hemisphere dome and fabricate it without the need of support structures. Two main fabrication strategies are developed: wedge-shaped partitioning, and a rotary toolpath. A five-axis toolpath and a 2+1+1-axis toolpath is introduced to fabricate the partitioned build scenarios. A rotary (1+3-axis) toolpath is also developed. It is concluded that planar slicing brings limitations to reduce the number of partitions that can be modified by a constant step over toolpath. On one hand, the partitioning strategy provides an opportunity to fabricate geometries in a supportless fashion by direct energy deposition additive manufacturing, on the other hand, it introduces physical properties challenges such as surface roughness and hardness variations. Process planning, data collection, and experimental/numerical procedures are implemented to investigate the surface roughness variations (Ra measurement) of fabricated domes. Hence, two solutions are developed using Matlab programming. A mount solution uses the magnified pictures of the exposed surface edges of mount samples as input data. The other solution uses a 3D point cloud of the surface. The innovation of the 3D point cloud solution is the distance factor that is applied in the calculations. The results of this solution are compared to the mount solution. Since the input data of the mount solution is more accurate, the results are more reliable than the 3D point cloud method. The Ra variation diagrams show lower Ra values for the 5-axis sample and the highest values for the rotary sample. Large surface irregularities are noticed at the transition points between partitions, which escalates the roughness values drastically in the region. The sudden alteration of the tool orientation between partitions causes these surface irregularities. Additionally, process planning, data collection, and experimental/numerical analyses are developed to explore hardness variations of the fabricated domes along the slicing direction. The hardness diagram of the 2+1+1-axis sample shows a recognizable pattern for partitions 2-4. The hardness is around 200 (HV) within the partitions but drops to 150 (HV) at the transition points between partitions. Partitions 5-8 show a less recognizable pattern. Although the rotary sample is fabricated in 3 intermittent fabrication sections, it does not show any significant pattern related to the sectioning. The statistical analysis of the hardness shows the highest standard deviation for the 5-axis sample and the least for the rotary one. Finite element analysis of the hardness and residual stress are performed by the ESI Sysweld software for 144 beads of the 2+1+1-axis sample. To reduce the calculation time (a factor of 15 times), a variable mesh size of the beads and substrate are introduced. This means that the element size of the beads grows for the regions farther from the measurement region. The resultant hardness diagram predicts the peak and valley of the experimental diagram for the partitions 1-4, but it misses some patterns for partitions 5-8. Fast Fourier transformation analyses of the surface roughness and experimental/numerical hardness data show a repetitive pattern by the wavelength of the partition length. The preparation time and accuracy of the finite element analysis results reveal that an experimental fabrication and measurement test is preferred at this time, or a new method of numerical analysis is required. This research clearly illustrates the challenges associated with building a complex component and understanding its characteristics. On one hand, splitting the part geometry by different partitioning shapes facilitates the fabrication of the geometries in a supportless fashion. However, this fabrication strategy introduces inconsistency in the mechanical properties. Hardness variations generated by a partitioning strategy needs to be dealt with (possibly by a post-heat treatment). Surface quality at the transient points needs to be investigated more. This foundational research highlights the process planning challenges associated with metal bead based deposition processes, and highlights relevant challenges for similar process families

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

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    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

    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

    Improvement of Geometric Quality Inspection and Process Efficiency in Additive Manufacturing

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    Additive manufacturing (AM) has been known for its ability of producing complex geometries in flexible production environments. In recent decades, it has attracted increasing attention and interest of different industrial sectors. However, there are still some technical challenges hindering the wide application of AM. One major barrier is the limited dimensional accuracy of AM produced parts, especially for industrial sectors such as aerospace and biomedical engineering, where high geometric accuracy is required. Nevertheless, traditional quality inspection techniques might not perform well due to the complexity and flexibility of AM fabricated parts. Another issue, which is brought up from the growing demand for large-scale 3D printing in these industry sectors, is the limited fabrication speed of AM processes. However, how to improve the fabrication efficiency without sacrificing the geometric quality is still a challenging problem that has not been well addressed. In this work, new geometric inspection methods are proposed for both offline and online inspection paradigms, and a layer-by-layer toolpath optimization model is proposed to further improve the fabrication efficiency of AM processes without degrading the resolution. First, a novel Location-Orientation-Shape (LOS) distribution derived from 3D scanning output is proposed to improve the offline inspection in detecting and distinguishing positional and dimensional non-conformities of features. Second, the online geometric inspection is improved by a multi-resolution alignment and inspection framework based on wavelet decomposition and design of experiments (DOE). The new framework is able to improve the alignment accuracy and to distinguish different sources of error based on the shape deviation of each layer. In addition, a quickest change point detection method is used to identify the layer where the earliest change of systematic deviation distribution occurs during the printing process. Third, to further improve the printing efficiency without sacrificing the quality of each layer, a toolpath allocation and scheduling optimization model is proposed based on a concurrent AM process that allows multiple extruders to work collaboratively on the same layer. For each perspective of improvements, numerical studies are provided to emphasize the theoretical and practical meanings of proposed methodologies

    3d Scanning And The Impact Of The Digital Thread On Manufacturing And Re-Manufacturing Applications

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    3D laser line scanners are becoming a powerful technology for capturing point cloud datasets and collecting dimensional information for many objects. However, the use of point cloud is limited due to many factors. These include the lack of on deep understanding of the effect of point cloud parameters on scan quality. This knowledge is critical to gaining an understanding of the measurement in point cloud. Currently, there are no adequate measurement procedures for 3D scanners. There is a need for standardized measurement procedures to evaluate 3D scanner accuracy due to uncertainties in 3D scanning, such as surface quality, surface orientation and scan depth [6]. The lack of standardized procedures does not allow the technology to be fully automated and used in manufacturing facilities that would allow 100% in-line inspection. In this dissertation I worked on accomplishing four tasks that will achieve the objective of having a standardized measurement procedure that is critical to develop an automated laser scanning system to avoid variations and have consistent data capable of identifying defects. The four tasks are: (1) linking the robot workspace with the scanner workspace; (2) studying the effect of the scanning speed and the resolution on point cloud quality by conducting an experiment with systematically varied scan parameters on scan quality; (3) studying the overall error of that is associated with the transformation of the point cloud in a remanufacturing facility using additive manufacturing. The parameters that were tested are the effect of view angle, standoff distance, speed, and resolution. Knowing the effect of these parameters is important in order to generate the scan path that provides the best coverage and quality of points collected. There is also a need to know the impact of all the scanning parameters especially the speed and the resolution; (4) modeling a machine learning tool to optimize the parameters of different scanning techniques after collecting the scanning results to select the optimal ones that provide the best scan quality. With the success of this work, the advancement and practice of automated quality monitoring in manufacturing will increase

    Meshless Additive Manufacturing

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    3D printing-as-a-service for collaborative engineering

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    3D printing or Additive Manufacturing (AM) are utilised as umbrella terms to denote a variety of technologies to manufacture or create a physical object based on a digital model. Commonly, these technologies create the objects by adding, fusing or melting a raw material in a layer-wise fashion. Apart from the 3D printer itself, no specialised tools are required to create almost any shape or form imaginable and designable. The possibilities of these technologies of these technologies are plentiful and cover the ability to manufacture every object, rapidly, locally and cost-efficiently without wasted resources and material. Objects can be created to specific forms to perform as perfectly fitting functions without consideration of the assembly process. To further the advance the availability and applicability of 3D printing, this thesis identifies the problems that currently exist and attempts to solve them. During the 3D printing process, data (i. e., files) must be converted from their original representation, e. g., CAD file, to the machine instructions for a specific 3D printer. During this process, information is lost, and other information is added. Traceability is lacking in 3D printing. The actual 3D printing can require a long period of time to complete, during which errors can occur. In 3D printing, these errors are often non-recoverable or reversible, which results in wasted material and time. In addition to the lack of closed-loop control systems for 3D printers, careful planning and preparation are required to avoid these costly misprints. 3D printers are usually located remotely from users, due to health and safety considerations, special placement requirements or out of comfort. Remotely placed equipment is impractical to monitor in person; however, such monitoring is essential. Especially considering the proneness of 3D printing to errors and the implications of this as described previously. Utilisation of 3D printers is an issue, especially with expensive 3D printers. As there are a number of differing 3D printing technologies available, having the required 3D printer, might be problematic. 3D printers are equipped with a variety of interfaces, depending on the make and model. These differing interfaces, both hard- and software, hinder the integration of different 3D printers into consistent systems. There exists no proper and complete ontology or resource description schema or mechanism that covers all the different 3D printing technologies. Such a resource description mechanism is essential for the automated scheduling in services or systems. In 3D printing services the selection and matching of appropriate and suitable 3D printers is essential, as not all 3D printing technologies are able to perform on all materials or are able to create certain object features, such as thin walls or hollow forms. The need for companies to sell digital models for AM will increase in scenarios where replacement or customised parts are 3D printed by consumers at home or in local manufacturing centres. Furthermore, requirements to safeguard these digital models will increase to avoid a repetition of the problems from the music industry, e. g., Napster. Replication and ‘theft’ of these models are uncontrollable in the current situation. In a service oriented deployment, or in scenarios where the utilisation is high, estimations of the 3D printing time are required to be available. Common 3D printing time estimations are inaccurate, which hinder the application of scheduling. The complete and comprehensive understanding of the complexity of an object is discordant, especially in the domain of AM. This understanding is required to both support the design of objects for AM and match appropriate manufacturing resources to certain objects. Quality in AM and FDM have been incompletely researched. The quality in general is increased with maturity of the technology; however, research on the quality achievable with consumer-grade 3D printers is lacking. Furthermore, cost-sensitive measurement methods for quality assessment are expandable. This thesis presents the structured design and implementation of a 3D printing service with associated contributions that provide solutions to particular problems present in the AM domain. The 3D printing service is the overarching component of this thesis and provides the platform for the other contributions with the intention to establish an online, cloud-based 3D printing service for use in end-user and professional settings with a focus on collaboration and cooperation

    New Methodology for Automatic Process Parameters Optimization in Selective Laser Melting

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    Selective laser melting is one of the most promising additive manufacturing technologies, thanks to its capability to manufacture complex shaped parts with good dimensional accuracy and high mechanical performance. In recent years, this technique is starting to be adopted for the production of end-use parts, addressing high quality requirements. To achieve the desired quality of the final product it is necessary to optimize the process parameters, possibly by reducing the build time needed for its production. However, the currently available process optimization methodologies are very time consuming and there is a lack of standards. The aim of this work is to develop an automatic, reliable and objective process optimization technique, which can be employed to find optimal parameters combinations for different process conditions. Therefore, it has been developed an experimental approach, based on single tracks analysis and on 3D benchmarks characterization. The main novelty of this optimization method is the automatization of samples analysis, which entailed the adoption of innovative surface metrology techniques and of novel algorithmic frameworks developed in MATLAB environment. According to the novel method, the effects of laser power (P), scan speed (v) and laser spot size (ds) have been investigated for the two most used materials; the extra-low-interstitial grade of Ti6Al4V alloy and the 316L stainless steel. Hence, P-v optimal combinations has been defined for each spot size level investigated, finding a first optimal region in single tracks analysis and then identifying the optimal parameter set for 3D components production. This methodology has allowed the definition of multiple optimal parameter sets in an automatic way, limiting time and material waste. Therefore, it can be adopted in all existent production strategies that require more than one process parameter set and could allow the development of new production approaches
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