7,185 research outputs found

    Knowledge-based Modelling of Additive Manufacturing for Sustainability Performance Analysis and Decision Making

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    Additiivista valmistusta on pidetty käyttökelpoisena monimutkaisissa geometrioissa, topologisesti optimoiduissa kappaleissa ja kappaleissa joita on muuten vaikea valmistaa perinteisillä valmistusprosesseilla. Eduista huolimatta, yksi additiivisen valmistuksen vallitsevista haasteista on ollut heikko kyky tuottaa toimivia osia kilpailukykyisillä tuotantomäärillä perinteisen valmistuksen kanssa. Mallintaminen ja simulointi ovat tehokkaita työkaluja, jotka voivat auttaa lyhentämään suunnittelun, rakentamisen ja testauksen sykliä mahdollistamalla erilaisten tuotesuunnitelmien ja prosessiskenaarioiden nopean analyysin. Perinteisten ja edistyneiden valmistusteknologioiden mahdollisuudet ja rajoitukset määrittelevät kuitenkin rajat uusille tuotekehityksille. Siksi on tärkeää, että suunnittelijoilla on käytettävissään menetelmät ja työkalut, joiden avulla he voivat mallintaa ja simuloida tuotteen suorituskykyä ja siihen liittyvän valmistusprosessin suorituskykyä, toimivien korkea arvoisten tuotteiden toteuttamiseksi. Motivaation tämän väitöstutkimuksen tekemiselle on, meneillään oleva kehitystyö uudenlaisen korkean lämpötilan suprajohtavan (high temperature superconducting (HTS)) magneettikokoonpanon kehittämisessä, joka toimii kryogeenisissä lämpötiloissa. Sen monimutkaisuus edellyttää monitieteisen asiantuntemuksen lähentymistä suunnittelun ja prototyyppien valmistuksen aikana. Tutkimus hyödyntää tietopohjaista mallinnusta valmistusprosessin analysoinnin ja päätöksenteon apuna HTS-magneettien mekaanisten komponenttien suunnittelussa. Tämän lisäksi, tutkimus etsii mahdollisuuksia additiivisen valmistuksen toteutettavuuteen HTS-magneettikokoonpanon tuotannossa. Kehitetty lähestymistapa käyttää fysikaalisiin kokeisiin perustuvaa tuote-prosessi-integroitua mallinnusta tuottamaan kvantitatiivista ja laadullista tietoa, joka määrittelee prosessi-rakenne-ominaisuus-suorituskyky-vuorovaikutuksia tietyille materiaali-prosessi-yhdistelmille. Tuloksina saadut vuorovaikutukset integroidaan kaaviopohjaiseen malliin, joka voi auttaa suunnittelutilan tutkimisessa ja täten auttaa varhaisessa suunnittelu- ja valmistuspäätöksenteossa. Tätä varten testikomponentit valmistetaan käyttämällä kahta metallin additiivista valmistus prosessia: lankakaarihitsaus additiivista valmistusta (wire arc additive manufacturing) ja selektiivistä lasersulatusta (selective laser melting). Rakenteellisissa sovelluksissa yleisesti käytetyistä metalliseoksista (ruostumaton teräs, pehmeä teräs, luja niukkaseosteinen teräs, alumiini ja kupariseokset) testataan niiden mekaaniset, lämpö- ja sähköiset ominaisuudet. Lisäksi tehdään metalliseosten mikrorakenteen karakterisointi, jotta voidaan ymmärtää paremmin valmistusprosessin parametrien vaikutusta materiaalin ominaisuuksiin. Integroitu mallinnustapa yhdistää kerätyn kokeellisen tiedon, olemassa olevat analyyttiset ja empiiriset vuorovaikutus suhteet, sekä muut tietopohjaiset mallit (esim. elementtimallit, koneoppimismallit) päätöksenteon tukijärjestelmän muodossa, joka mahdollistaa optimaalisen materiaalin, valmistustekniikan, prosessiparametrien ja muitten ohjausmuuttujien valinnan, lopullisen 3d-tulosteun komponentin halutun rakenteen, ominaisuuksien ja suorituskyvyn saavuttamiseksi. Valmistuspäätöksenteko tapahtuu todennäköisyysmallin, eli Bayesin verkkomallin toteuttamisen kautta, joka on vankka, modulaarinen ja sovellettavissa muihin valmistusjärjestelmiin ja tuotesuunnitelmiin. Väitöstyössä esitetyn mallin kyky parantaa additiivisien valmistusprosessien suorituskykyä ja laatua, täten edistää kestävän tuotannon tavoitteita.Additive manufacturing (AM) has been considered viable for complex geometries, topology optimized parts, and parts that are otherwise difficult to produce using conventional manufacturing processes. Despite the advantages, one of the prevalent challenges in AM has been the poor capability of producing functional parts at production volumes that are competitive with traditional manufacturing. Modelling and simulation are powerful tools that can help shorten the design-build-test cycle by enabling rapid analysis of various product designs and process scenarios. Nevertheless, the capabilities and limitations of traditional and advanced manufacturing technologies do define the bounds for new product development. Thus, it is important that the designers have access to methods and tools that enable them to model and simulate product performance and associated manufacturing process performance to realize functional high value products. The motivation for this dissertation research stems from ongoing development of a novel high temperature superconducting (HTS) magnet assembly, which operates in cryogenic environment. Its complexity requires the convergence of multidisciplinary expertise during design and prototyping. The research applies knowledge-based modelling to aid manufacturing process analysis and decision making in the design of mechanical components of the HTS magnet. Further, it explores the feasibility of using AM in the production of the HTS magnet assembly. The developed approach uses product-process integrated modelling based on physical experiments to generate quantitative and qualitative information that define process-structure-property-performance interactions for given material-process combinations. The resulting interactions are then integrated into a graph-based model that can aid in design space exploration to assist early design and manufacturing decision-making. To do so, test components are fabricated using two metal AM processes: wire and arc additive manufacturing and selective laser melting. Metal alloys (stainless steel, mild steel, high-strength low-alloyed steel, aluminium, and copper alloys) commonly used in structural applications are tested for their mechanical-, thermal-, and electrical properties. In addition, microstructural characterization of the alloys is performed to further understand the impact of manufacturing process parameters on material properties. The integrated modelling approach combines the collected experimental data, existing analytical and empirical relationships, and other data-driven models (e.g., finite element models, machine learning models) in the form of a decision support system that enables optimal selection of material, manufacturing technology, process parameters, and other control variables for attaining desired structure, property, and performance characteristics of the final printed component. The manufacturing decision making is performed through implementation of a probabilistic model i.e., a Bayesian network model, which is robust, modular, and can be adapted for other manufacturing systems and product designs. The ability of the model to improve throughput and quality of additive manufacturing processes will boost sustainable manufacturing goals

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

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    Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex system simulation and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems

    Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

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    Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

    Statistical Modeling and Analysis

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    Die Blockchain-Technologie revolutioniert die Interaktion zwischen Menschen durch Peer-to-Peer-Netzwerke, Kryptografie und Konsensalgorithmen. Trustless Trust ermöglicht sichere und transparente Transaktionen ohne Zwischenhändler. Trotz der zunehmenden Beliebtheit von Krypto-Assets und den damit verbundenen „Tokenomics“ hat die Öffentlichkeit immer noch kein umfangreiches Wissen über die Funktionsweisen dieser Technologie, und ein Großteil des Diskurses bleibt spekulativ. Das Hauptziel dieser Arbeit ist, die grundlegenden Prinzipien von Krytowährungen (Cryptos) und Non-Fungible Tokens (NFTs) zu untersuchen sowie eine Korrelation zwischen der Technologie und ihren Auswirkungen auf die Wirtschaft aus statistischer und wirtschaftlicher Sicht herzustellen. Um dieses Ziel zu erreichen, wird in den Kapiteln 2 und 3 der Einfluss der Blockchain-Technologie auf Ökonomie und Funktionsweise von Kryptowährungen anhand ökonometrischer Modelle und Clustering-Techniken untersucht. Kapitel 3 untersucht Kryptowirschaft und Blockchain-Funktionalität anhand empirischer Methoden, insbesondere für Coincreatoren und Investoren. Wir zeigen am Beispiel von Ethereum, dass die wirtschaftliche Leistung von Kryptowährungen durch die Gestaltung der ihnen zugrunde liegenden Blockchain-Technologie beeinflusst werden kann. Kapitel 4 untersucht die partiellen Korrelationen von Bitcoin-Renditen über neun verschiedene Zentralbörsen aus der Perspektive eines hochfrequenten, dynamischen Netzwerks. Die vorgeschlagene MHAR-CM liefert Kovarianzschätzungen, die die Besonderheiten der Kryptomärkte berücksichtigen. Das Kapitel zeigt Spillover- und Third-Party-Risiken zwischen diesen Börsen. Kapitel 5 verwendet eine Hedonische Bewertungsmethode, um den DAI Digital Art Index basierend auf dem NFT-Kunstmarkt zu konstruieren. Ein besonderer Fokus liegt auf der Nivellierung der Auswirkungen von Ausreißern mit einer einstufigen robusten Regressions-Huberisierung und einem dynamic conditional score model. Diese Arbeit verknüpft neue Technologien und Wirtschaft durch statistische Modellierung und Analyse. Durch die Bereitstellung empirischer Belege beobachten wir, wie die Blockchain-Technologie unsere Wahrnehmung von Geld, Kunst und anderen Branchen verändert.The emergence of distributed ledger technologies, such as blockchain, has revolutionized how individuals interact by enabling "trust-less trust" through peer-to-peer networks, cryptography, and consensus algorithms. This technology eliminates intermediaries and provides secure, transparent transaction methods. However, public understanding of this technology, along with "Tokenomics", remains limited, resulting in speculative discourse. The main objective of this thesis is to investigate the fundamental principles of cryptocurrencies (cryptos) and non-fungible tokens (NFTs) and establish a correlation between the technology and its economic impact from statistical and economic perspectives. To achieve this, Chapters 2 and 3 explore the influence of blockchain technology on the economic and functional performance of cryptos using econometric models and clustering techniques. Chapter 3 presents an empirical framework that offers insights to coin creators and investors regarding the interplay between cryptonomics, blockchain functionality, and market dynamics. The economic performance of cryptocurrencies, illustrated with Ethereum as an example, is shown to be affected by the design of their underlying blockchain technology. Chapter 4 examines partial correlations of Bitcoin returns across nine centralized exchanges from a high-frequency dynamic network perspective. The proposed MHAR-CM provides reasonable covariance estimates that account for the unique characteristics of crypto markets. This chapter uncovers spillover risk and counterparty risk among these exchanges. In Chapter 5, a hedonic regression approach is employed to construct the DAI digital art index for the NFT art market. Special emphasis is given to mitigating the impact of outliers using one-step robust regression Huberization and a dynamic conditional score model. The DAI index enhances our understanding of this emerging art market and facilitates observation of its macroeconomic trends. This thesis establishes a connection between emerging technologies and the economy through statistical modeling and analysis. By providing empirical evidence, we gain insights into how blockchain technology is transforming our perceptions of money, art, and various industries

    OCM 2023 - Optical Characterization of Materials : Conference Proceedings

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    The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field
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