76 research outputs found

    Advanced Characterization and On-Line Process Monitoring of Additively Manufactured Materials and Components

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    This reprint is concerned with the microstructural characterization and the defect analysis of metallic additively manufactured (AM) materials and parts. Special attention is paid to the determination of residual stress in such parts and to online monitoring techniques devised to predict the appearance of defects. Finally, several non-destructive testing techniques are employed to assess the quality of AM materials and parts

    3D‐printing with steel on thin sheets for application in free form façade construction: welding process development and material properties

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    Wire and Arc Additive Manufacturing (WAAM) became a significant research field in structural engineering over the past years. It offers the opportunity to apply material fast and precisely, following the load path, inducing effective material consumption. Considering free‐formed sheet‐metal façades, to date large thicknesses are necessary to maintain the final shape and load‐bearing capacity that however causes high material consumption and costs during the construction processes. Hence, WAAM could be an opportunity to reduce the amount of mild steel used for free‐form façades. As part of a research project at TU Darmstadt, 1 mm thick free‐form sheet‐metal façades elements have been manufactured using WAAM. Due to precisely welded lattice structures, placed on the backside of the metal sheet, the reinforcement will be ensured. Wide studies on welding parameters focused on a stable process without damaging the later frontside of the façade panel and on process optimization. In addition, material properties of the welding material have been determined to check its usability in façade construction

    Geometry Prediction in Wire Arc Additive Manufacturing Using Machine Learning

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    Wire Arc Additive Manufacturing has disruptive potential for modern manufacturing. The technology comes with the flexibility and material efficiency of additive manufacturing processes while mitigating the disadvantages through high material output and high energy efficiency. The prevalence of the technology is inhibited by the large induced residual stresses and geometrical inaccuracy. This work tackles the latter by assessing the process parameter-geometry relationship using Machine Learning (ML) algorithms. To do so, multiple mild steel welding beads with varying shape features like corner angle are printed using a Metal Inert Gas (MIG) welding machine attached to an industrial robot. The cross sectional profile of the printed beads is measured using a point laser sensor and correlated through different ML algorithms to input features such as travel speed (TS), wire feed speed (WFS), interlayer temperature, and shape features. By incorporating varying bead shapes, a holistic model, not limited to geometry prediction of straight beads only, is trained. Thus, the model holds the potential to learn the process parameter-geometry relationship for different shape features of a part. Using the model, excess material at the inner angle of corners determined by the overlapping regions of the two adjacent beads can be predicted. By generating a database of possible bead shapes a inverse algorithm was created, that suggests welding parameter combinations resulting in a smoother bead shape at corners. Additionally, a study on the transferability of common bead geometry prediction models on other research testbeds was conducted. The importance of input features for transferability is assessed and the potential to increase transferability by infusing the model training with mass conservation is examined.M.S

    In Situ Process Monitoring and Machine Learning Based Modeling of Defects and Anomalies in Wire-Arc Additive Manufacturing

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    Wire Arc Additive Manufacturing (WAAM) has made great strides in recent years however, there remain numerous persistent challenges still hindering more widespread adoption. Defects in the parts produced degrade their mechanical performance. Inconsistency in the geometry of the weld beads or undesirable anomalies such as waviness, or humps can lead to loss of geometric accuracy and in extreme cases, when anomalies propagate to subsequent layers, build failure. Such defects can be mitigated by a controls framework, which would require a model that maps undesirable outcomes to information about the process that can be obtained in real time. This thesis explores the development of a multi-sensor framework for real time data acquisition and several approaches for arriving at such a model, employing well known machine learning methodologies including Random Forests, Artificial Neural Networks and Long Short Term Memory. The merits and drawbacks of these methods is discussed, and a physics based approach intended to mitigate some of the drawbacks is explored. The models are trained first on data obtained on a single build layer, and subsequently on a multi-layer wall

    Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

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    Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

    The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

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    The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

    Additive Manufacturing of Metals

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    This book is an exciting collection of research articles that offer a unique view into the fast developing field of metal additive manufacturing, providing insights into this advanced manufacturing technology. The articles span recent advances in metal AM technologies, and their application to a wide range of metals, exploring how the processing parameters offer unique material properties. This book encapsulates the state of the art in this rapidly evolving field of technology and will be a valuable resource for researchers in the field, from Ph.D. students to professors, and through to industrial end users

    Improvement and tailoring of parts fabricated with Wire and Arc Additive Manufacturing

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    Wire and arc additive manufacturing (WAAM) is an Additive Manufacturing (AM) process that finds applications in different industrial sectors. It shows to be competitive compared to other AM technologies, mainly due to its low implementation costs, high deposition rates, and the ability to produce medium to large complex parts. Improvement of parts’ properties has been the primary goal of the scientific community and was also the main objective of this work, where significant scientific and technological developments were implemented. Different approaches were tested: i) by performing heat treatments; ii) by adding grain refiners to the molten metal; iii) by fabricating Functionally Graded Materials (FGM); iv) by developing a new process variant called Ultra cold Wire and Arc Additive Manufacturing (UC-WAAM). To fulfill these objectives, laboratory means were developed. Including the development of a multi-wire gas tungsten arc welding (GTAW) torch for creating components with a functional gradient, two prototypes to introduce ceramic particles in the molten pool, and a customized gas metal arc welding (GMAW) torch. The feasibility of using ceramic particles to refine the grain structure of WAAM parts was experimentally demonstrated on a High Strength Low Alloy (HSLA) steel and Inconel 625. Parts characterization was performed with multi-phenomena techniques: optical and scanning electron microscopy assisted by energy dispersive spectroscopy and electron backscatter diffraction, synchrotron X-ray diffraction, and mechanical testing. Despite the potential applications of FGM, one of the main limitations is related to significant stresses, chemical incompatibilities, and the possible formation of undesirable intermetallics. In this work, manufacturing of different FGM was successfully produced without defects by applying different building strategies. The results showed that a direct-type interface resulted in superior mechanical properties without intermetallics and smaller residual stresses than a smooth transition build-type. The development of UC-WAAM lowered the average temperatures experienced by the material and increased the cooling rates during parts fabrication compared to traditional GMAW. An overhang structure was fabricated, highlighting the potential for UC-WAAM to be used for this type of structures.A deposição direta de energia por arco elétrico e fio é um processo de fabrico aditivo que tem encontrando diferentes aplicações em diversos setores industriais. Esta tecnologia tem-se mostrado competitiva em comparação com outras tecnologias de fabrico aditivo, devido aos seus baixos custos de implementação, altas taxas de deposição e capacidade de produzir peças complexas de média/elevada dimensão. A melhoria das propriedades das peças tem sido o principal objetivo da comunidade científica e como tal foi também o principal objetivo deste trabalho, onde foram realizados importantes desenvolvimentos científicos e tecnológicos. Diferentes abordagens foram testadas: i) através de tratamentos térmicos; ii) através da adição de afinadores de grão ao banho de fusão; iii) através do desenvolvimento de materiais com gradiente de funcionalidade (FGM); iv) através do desenvolvimento de uma nova variante de processo denominada Ultra Cold Wire and Arc Additive Manufacturing (UC-WAAM). De forma a cumprir os objetivos propostos, foram desenvolvidos meios laboratoriais, tais como o desenvolvimento de uma tocha costumizável de alimentação multifio de soldadura com um elétrodo não consumível de tungstênio para a criação de componentes com gradiente funcional. Foram ainda desenvolvidos dois protótipos para introduzir partículas cerâmicas no banho de fusão, e uma tocha de soldadura por arco elétrico com gás de proteção. A viabilidade do uso de partículas cerâmicas para refinar a estrutura de peças produzidas por fabrico aditivo por arco elétrico foi demonstrada experimentalmente num aço de baixa liga alta resistência e no Inconel 625. A caracterização das peças foi realizada com diferentes técnicas, tais como microscópio ótico e eletrónico de varrimento equipado com espectroscopia de energia dispersiva de raios-X, difração de eletrões retrodifundidos, difração de raios X utilizando radiação sincrotrão, e através de diferentes ensaios mecânicos. Apesar das potenciais aplicações de materiais com gradiente de funcionalidade, uma das suas principais limitações está relacionada com as elevadas tensões residuais, incompatibilidades químicas e a potencial formação de intermetálicos indesejáveis. Neste trabalho, foram produzidos com sucesso e sem defeitos diversos materiais com gradiente funcionalidade através de diferentes estratégias de deposição. Os resultados obtidos demonstraram que uma interface do tipo direta resultou em propriedades mecânicas superiores, sem compostos intermetálicos e com tensões residuais inferiores do que uma transição gradual entre os materiais utilizados. O desenvolvimento de uma nova variante de processo permitiu reduzir as temperaturas e as taxas de arrefecimento que se desenvolvem durante a fabricação de peças comparativamente com a soldadura convencional por arco elétrico com gás de proteção. Foi também fabricada uma estrutura sem suporte, destacando o potencial desta nova variante para o fabrico deste tipo de componentes

    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

    Additive Manufacturing Research and Applications

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    This Special Issue book covers a wide scope in the research field of 3D-printing, including: the use of 3D printing in system design; AM with binding jetting; powder manufacturing technologies in 3D printing; fatigue performance of additively manufactured metals, such as the Ti-6Al-4V alloy; 3D-printing methods with metallic powder and a laser-based 3D printer; 3D-printed custom-made implants; laser-directed energy deposition (LDED) process of TiC-TMC coatings; Wire Arc Additive Manufacturing; cranial implant fabrication without supports in electron beam melting (EBM) additive manufacturing; the influence of material properties and characteristics in laser powder bed fusion; Design For Additive Manufacturing (DFAM); porosity evaluation of additively manufactured parts; fabrication of coatings by laser additive manufacturing; laser powder bed fusion additive manufacturing; plasma metal deposition (PMD); as-metal-arc (GMA) additive manufacturing process; and spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning
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