11,885 research outputs found

    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

    Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transition and the second-order phase transition in the XXZ models

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    We propose two machine-learning methods based on neural networks, which we respectively call the phase-classification method and the temperature-identification method, for detecting different types of phase transitions in the XXZ models without prior knowledge of their critical temperatures. The XXZ models have exchange couplings which are anisotropic in the spin space where the strength is represented by a parameter Δ(>0)\Delta(>0). The models exhibit the second-order phase transition when Δ>1\Delta>1, whereas the Berezinskii-Kosterlitz-Thouless (BKT) phase transition when Δ<1\Delta<1. In the phase-classification method, the neural network is trained using spin or vortex configurations of well-known classical spin models other than the XXZ models, e.g., the Ising models and the XY models, to classify those of the XXZ models to corresponding phases. We demonstrate that the trained neural network successfully detects the phase transitions for both Δ>1\Delta>1 and Δ<1\Delta<1, and the evaluated critical temperatures coincide well with those evaluated by conventional numerical calculations. In the temperature-identification method, on the other hand, the neural network is trained so as to identify temperatures at which the input spin or vortex configurations are generated by the Monte Carlo thermalization. The critical temperatures are evaluated by analyzing the optimized weight matrix, which coincide with the result of numerical calculation for the second-order phase transition in the Ising-like XXZ model with Δ=1.05\Delta=1.05 but cannot be determined uniquely for the BKT transition in the XY-like XXZ model with Δ=0.95\Delta=0.95.Comment: 17 pages, 12 figure

    Efficient instance and hypothesis space revision in Meta-Interpretive Learning

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    Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL. First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces

    CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

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    This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship

    Modeling, Simulation and Data Processing for Additive Manufacturing

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    Additive manufacturing (AM) or, more commonly, 3D printing is one of the fundamental elements of Industry 4.0. and the fourth industrial revolution. It has shown its potential example in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing and local on-demand manufacturing are some of the current benefits. Businesses based on AM have experienced double-digit growth in recent years. Accordingly, we have witnessed considerable efforts in developing processes and materials in terms of speed, costs, and availability. These open up new applications and business case possibilities all the time, which were not previously in existence. Most research has focused on material and AM process development or effort to utilize existing materials and processes for industrial applications. However, improving the understanding and simulation of materials and AM process and understanding the effect of different steps in the AM workflow can increase the performance even more. The best way of benefit of AM is to understand all the steps related to that—from the design and simulation to additive manufacturing and post-processing ending the actual application.The objective of this Special Issue was to provide a forum for researchers and practitioners to exchange their latest achievements and identify critical issues and challenges for future investigations on “Modeling, Simulation and Data Processing for Additive Manufacturing”. The Special Issue consists of 10 original full-length articles on the topic

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Numerical methods for optimal transport and optimal information transport on the sphere

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    The primary contribution of this dissertation is in developing and analyzing efficient, provably convergent numerical schemes for solving fully nonlinear elliptic partial differential equation arising from Optimal Transport on the sphere, and then applying and adapting the methods to two specific engineering applications: the reflector antenna problem and the moving mesh methods problem. For these types of nonlinear partial differential equations, many numerical studies have been done in recent years, the vast majority in subsets of Euclidean space. In this dissertation, the first major goal is to develop convergent schemes for the sphere. However, another goal of this dissertation is application-centered, that is evaluating whether the partial differential equation techniques using Optimal Transport are actually the best methods for solving such problems. The reflector antenna is an optics inverse problem where one finds the shape of a reflector surface in order to refocus light into a prescribed far-field output intensity. This problem can be solved using Optimal Transport. The moving mesh methods problem is an adaptive mesh technique where one redistributes the density of the vertices of a mesh without tangling the edges connecting the vertices. Both Optimal Transport and Optimal Information Transport approaches can be used in solving this problem. The Monge Problem of Optimal Transport is concerned with computing the “optimal” mapping between two probability distributions. This actually can define a Riemannian distance between probability measures in a probability space. An-other choice of Riemannian metric on this space, the infinite-dimensional Fisher-Rao metric, gives an “information geometric” structure to the space of probability measures. It turns out that a simple partial differential equation can be solved for a mapping that relates to the underlying information geometry given by the Fisher-Rao metric. Solving for such an “information geometric” mapping is known as Optimal Information Transport. In this dissertation, a convergence framework is first established for com-puting the solution to the partial differential equation formulation of Optimal Transport on the sphere. This convergence framework uses geodesic normal coor-dinates to perform computations in local tangent planes. The numerical scheme also has a control on the Lipschitz constant of the discrete solution, which allows a convergence theorem for consistent and monotone discretizations to be proved in the absence of a comparison principle for the partial differential equation. Then, a finite-difference scheme for the partial differential equation formulation of Opti-mal Transport on the sphere is constructed which satisfies the hypotheses of the convergence theorem. An explicit formula for the mixed Hessian term is derived for two different cost functions. In order to construct a monotone discretization, discrete Laplacian terms are carefully added into the scheme. Current work has established convergence rates for solutions of monotone discretizations of linear elliptic partial differential equations on compact 2D manifolds without boundary. The goal is to then generalize these linearized arguments for the Optimal Transport case on the sphere. Computations are performed for the reflector antenna problem. Other ad hoc schemes exist for computing the reflector antenna problem, but the proposed scheme is the most efficient provably convergent scheme. Further adaptations are made that allow for the scheme to deal with non-smooth cases more explicitly. For the moving mesh methods problem, a comparison of computations via Optimal Transport and Optimal Information Transport is performed for the sphere using provably convergent monotone schemes for both computations. These comparisons show the merits of using Optimal Information Transport for some challenging computations. Optimal Information Transport also seems like a natural generalization to other compact 2D surfaces beyond the sphere

    Rule-Based Policy Interpretation and Shielding for Partially Observable Monte Carlo Planning

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    Partially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm that can generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. However, the lack of an explicit representation of the policy hinders interpretability. In this thesis, we propose a methodology based on Maximum Satisfiability Modulo Theory (MAX-SMT) for analyzing POMCP policies by inspecting their traces, namely, sequences of belief-action pairs generated by the algorithm. The proposed method explores local properties of the policy to build a compact and informative summary of the policy behaviour. This representation exploits a high-level description encoded using logical formulas that domain experts can provide. The final formula can be used to identify unexpected decisions, namely, decisions that violate the expert indications. We show that this identification process can be used offline (to improve the explainability of the policy and to identify anomalous behaviours) or online (to shield the decisions of the POMCP algorithm). We also present an active methodology that can effectively query a POMCP policy to build more reliable descriptions quickly. We extensively evaluate our methodologies on two standard benchmarks for POMDPs, namely, emph{tiger} and emph{rocksample}, and on a problem related to velocity regulation in mobile robot navigation. Results show that our approach achieves good performance due to its capability to exploit experts' knowledge of the domains. Specifically, our approach can be used both to identify anomalous behaviours in faulty POMCPs and to improve the performance of the system by using the shielding mechanism. In the first case, we test the methodology against a state-of-the-art anomaly detection algorithm, while in the second, we compared the performance of shielded and unshielded POMCPs. We implemented our methodology in CC, and the code is open-source and available at href{https://github.com/GiuMaz/XPOMCP}{https://github.com/GiuMaz/XPOMCP}

    Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above
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