1,721,014 research outputs found
Robust circle reconstruction with the Riemann fit
Get PDFFinding and fitting circles from a set of points is a frequent problem in the data analysis of high-energy physics experiments. In a tracker immersed in a homogeneous magnetic field, tracks are close to perfect circles if projected to the bending plane. In a ring-imaging Cherenkov (RICH) detector, circles of photons around the crossing point of charged particles have to be found and their radii estimated. In both cases, non-negligible background may be present that tends to complicate the pattern recognition and to bias the circle fit. In this contribution we present a robust circle fit based on a modified Riemann fit that removes or significantly reduces the effect of background points. As in the standard Riemann fit, the measured points are projected to the Riemann sphere or paraboloid, and a plane is fitted to the projected points. The fit is made robust by replacing the usual least-squares regression by a least median of squares (LMS) regression. Because of the high breakdown point of the LMS estimator, the fit is insensitive to background points. The LMS plane is used to initialize the weights of an M-estimator that refits the plane in order to suppress eventual remaining outliers and to obtain the final circle parameters. The method is demonstrated on three sets of artificial data: points on a circle plus a comparable number of background points; points on two overlapping circles with additional background; and points obtained by the simulation of tracks in a drift chamber with mirror points and additional background. The results show high circle finding efficiency and small contamination of the final fitted circles.publishedVersionContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI
Three-Dimensional Analysis of Porosity in As-Manufactured Glass Fiber/Vinyl Ester Filament Winded Composites Using X-Ray Micro-Computed Tomography
Get PDFFilament winding is a technique to manufacture tubular composite structures and, therefore, is among the most appealing techniques for fabricating critical structures such as hollow tubes. Despite the recent advances, these structures are prone to a varying degree of porosity that may affect their mechanical performance. Therefore, the accurate detection and quantification of the manufacturing porosity is crucial. Micro-CT is most suitable for performing this activity at various scales. This work employs micro-CT for studying porosity inside an as-manufactured filament-winded composite structure. Void characteristics like volume, orientation, size, and relative volume fraction inside the hoop and helical layers are quantified inside a representative curved panel extracted from a glass fiber-vinyl ester tubular composite structure, which has not been studied in detail previously. It was observed that most voids are present in the matrix region. The voids are elliptical rod-like and spherical, with the latter present in the helical layers, which also host the majority of voids and the highest void volume fractions. The voids are highly aligned along the fiber orientation direction with higher misorientations for helical layers than the hoop layer. Large voids in base layers were created due to gaps formed during the winding process. Hence, the main goal of this study is to measure the voids' characteristics and the volumetric fraction during the stacking of filament wound hoop and helical layers during a generic filament winding pattern. The data can be further exploited as input for modeling filament winded composites in the presence of voids by researchers.publishedVersio
Atomistic modelling of Fe-Al and α-AlFeSi intermetallic compound interfaces
Get PDFThe joining of aluminum and steel has been considered an efficient solution for building light-weight technology, particularly in the automotive, aerospace and shipbuilding industries. It is an immense challenge to join these materials together due to the significant differences in the physical and chemical properties of aluminum and steel. The development of intermetallic compound (IMC) layers has a huge impact on the strength of the aluminum-steel joint. The development of IMCs at the aluminum and steel joint is greatly influenced by the welding methodology and temperature reached during the welding process. It is thermodynamically possible to develop certain IMCs depending on the composition and phase diagram of aluminum and steel alloys. For this reason, understanding the mechanical nature of the IMCs is pivotal to improve the welding methodologies.
In this work, atomistic simulations were performed on Fe2Al5, Fe4Al13 and α-AlFeSi bulk and interface structures. We started with the construction of atomistic bulk structures of Fe2Al5 and Fe4Al13 and calculated the mechanical properties using density functional theory (DFT) calculations. A comparative study was performed to identify the mechanical behavior of these compounds. Moreover, comparisons were also made with other experimental, semi-empirical and ab-initio methods to test the reliability of the calculations.
Due to the complex nature and large atomic structures of Fe-Al IMCs, using ab-initio methods could be very computationally expensive. To make computational calculations fast and accurate, a semi-empirical potential based method has also been used in this work. The main objective of this study was to test the reliability of modified embedded atoms method (MEAM) potentials and suitability for finding good initial structures for Fe-Al interfaces. It was concluded that MEAM and semi-empirical methods are not reliable for inferring mechanical features of Fe-Al IMCs. However, MEAM was found to be reasonable for finding good initial guesses for the Fe-Al interface structures.
Lastly, a systematic study was performed to identify the virtual tensile and shear strengths of Fe-Al and α-AlFeSi interfaces using DFT. Interface structures were optimized using the fast inertial relaxation engine (FIRE), which was very successful in optimizing these complex interfaces with a large number of atoms. After the optimization of the interface structures, virtual tensile and shear strength calculations were performed. An extended version of the so-called Universal Binding Energy relation (UBER) was used to fit the energy-displacement curve for virtual tensile strength and a Fourier series for the virtual shear strength predictions. The results indicated the potential negative effect of the Fe-Al IMCs on the strengths of the aluminum-steel joint
Functional and Optical Properties of Structured Surfaces in Additive Manufacturing: Technologies for appearance evaluation in the quality assurance of 3D-printed polymers
No full textQuality assurance of additively manufactured parts requires assessment of appearance and surface finish quality. Appearance investigation is a primary step to ensure that the manufactured parts meet the required structural, functional, and dimensional specifications, as well as possess the desired aesthetic qualities. Among the attributes considered when assessing an appearance are color, gloss, haze, translucency, texture, and surface finish. In advanced manufacturing, this process is particularly critical in industries where consumer perception and brand image are crucial, such as biomedical, automotive, aerospace, and consumer-oriented applications.
This thesis explores studies concerning structured surfaces in the additive manufacturing of polymers. During this project, the primary components of the manufacturing process were examined and considered, including materials, design, production, quality control, and optimization. Experimental and statistical analyzes were conducted to determine the optical and functional properties of the additively manufactured layers and how primary processing parameters affect additive manufacturing. A number of additive manufacturing (AM) machines were investigated, including Fused Filament Fabrication (FFF), Material Jetting (MJT), Stereolithography (SLA), and Selective Laser Sintering (SLS). PolyJet as an MJT technology was considered the specialization. Several conclusions were drawn from the investigations and findings are reported for further development of PolyJet technology.
An optimization and enhancement method has been developed to improve the properties of the material and the appearance of the final parts over a more extended period. Several properties were considered, such as optical properties, mechanical properties, life cycle response, and implementation of digital materials used for prototyping, medical purposes, and harsh environmental applications. A long-term descriptive study examined the color appearance, tensile behavior, and glass transition temperature of MJT objects in weathering chambers. Specific challenges were identified, and solutions were proposed according to Taguchi analysis to meet the quality goal of advanced manufacturing.
An extensive literature review was conducted to examine the surface characteristics of 3Dprinted objects with a particular focus on surface roughness. A comparative study has been conducted to determine the manufacturing factors influencing the lifetime, surface quality, and dimensional accuracy. Further investigations addressed these challenges, resulting in a comprehensive overview of the available AM techniques and identifying the most critical parameters influencing surface roughness. We have addressed the complexity of appearance assessment and the need for interpreting data and correlating them using multivariate statistical analysis. It has been discussed how printing parameters should be incorporated into advanced appearance modeling, taking into consideration that MJT objects possess complex appearances, are semi-translucent, and require a comprehensive study of their appearance. For instance, the build orientation and wedge angle determine whether we use cost-effective reflectance models, such as bidirectional reflectance distribution function (BRDF), or more sophisticated, accurate, and expensive models, such as bidirectional surface scattering distribution functions (BSSRDF), depending on the application requirements. In addition, it has been demonstrated that MJT is a promising technique that provides realistic objects that have very low appearance deficiencies, which is necessary for expanding the application of additive manufacturing.
Many other aspects of appearance in additive manufacturing were examined in conceptual investigations, including gloss, haze, translucency, texture, and reflectance modeling. This led to a deeper understanding of total control over appearance during prototyping and design. An industry 4.0 manufacturing environment will rely heavily on the latter. Compared to the other studies examining optical properties, these investigations focused primarily on appearance behavior rather than general applications in optics or for mechanical purposes. The differences and challenges of these results are more relevant to human perception of appearance than laser-based applications in photonic
A Deterministic Annealing PMHT Algorithm with an Application to Particle Tracking
No full textWe introduce the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementat- ion of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT). We introduce the Probabilistic Multi-Hypot- hesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementation of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT)
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
Get PDFThis open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topics are pattern recognition for track and vertex finding, solving the equations of motion by analytical or numerical methods, treatment of material effects such as multiple Coulomb scattering and energy loss, and the estimation of track and vertex parameters by statistical algorithms. The material covers both established methods and recent developments in these fields and illustrates them by outlining exemplary solutions developed by selected experiments. The clear presentation enables readers to easily implement the material in a high-level programming language. It also highlights software solutions that are in the public domain whenever possible. It is a valuable resource for PhD students and researchers working on online or offline reconstruction for their experiments
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
No full textIntro -- Preface -- Scope -- Content -- Audience -- Acknowledgements -- A Note on the References -- Typesetting and Notation -- Contents -- List of Figures -- List of Tables -- Part I Introduction -- 1 Tracking Detectors -- 1.1 Introduction -- 1.2 Gaseous Tracking Detectors -- 1.2.1 Multi-wire Proportional Chamber -- 1.2.2 Planar Drift Chamber -- 1.2.3 Cylindrical Drift Chamber -- 1.2.4 Drift Tubes -- 1.2.5 Time Projection Chamber -- 1.2.6 Micro-pattern Gas Detectors -- 1.3 Semiconductor Tracking Detectors -- 1.3.1 Silicon Strip Sensors -- 1.3.2 Hybrid Pixel Sensors -- 1.3.3 Silicon Drift Sensors -- 1.4 Scintillating Fiber Trackers -- 1.5 Alignment -- 1.6 Tracking Systems -- 1.6.1 Detectors at the LHC -- 1.6.1.1 ALICE -- 1.6.1.2 ATLAS -- 1.6.1.3 CMS -- 1.6.1.4 LHCb -- 1.6.2 Belle II and CBM -- 1.6.2.1 Belle II -- 1.6.2.2 CBM -- References -- 2 Event Reconstruction -- 2.1 Trigger and Data Acquisition -- 2.1.1 General Remarks -- 2.1.2 The CMS Trigger System -- 2.1.3 The LHCb Trigger System -- 2.2 Track Reconstruction -- 2.3 Vertex Reconstruction -- 2.4 Physics Objects Reconstruction -- 2.4.1 Particle ID by Dedicated Detectors -- 2.4.2 Particle and Object ID by Tracking and Calorimetry -- References -- 3 Statistics and Numerical Methods -- 3.1 Function Minimization -- 3.1.1 Newton-Raphson Method -- 3.1.2 Descent Methods -- 3.1.2.1 Line Search -- 3.1.2.2 Steepest Descent -- 3.1.2.3 Quasi-Newton Methods -- 3.1.2.4 Conjugate Gradients -- 3.1.3 Gradient-Free Methods -- 3.2 Statistical Models and Estimation -- 3.2.1 Linear Regression Models -- 3.2.2 Nonlinear Regression Models -- 3.2.3 State Space Models -- 3.2.3.1 Linear State Space Models and the Kalman Filter -- 3.2.3.2 Nonlinear State Space Models and the Extended Kalman Filter -- 3.3 Clustering -- 3.3.1 Hierarchical Clustering -- 3.3.2 Partitional Clustering -- 3.3.3 Model-Based ClusteringReferences -- Part II Track Reconstruction -- 4 Track Models -- 4.1 The Equations of Motion -- 4.2 Track Parametrization -- 4.3 Track Propagation -- 4.3.1 Homogeneous Magnetic Fields -- 4.3.2 Inhomogeneous Magnetic Fields -- 4.3.2.1 Runge-Kutta Methods -- 4.3.2.2 Approximate Analytical Formula -- 4.4 Error Propagation -- 4.4.1 Homogeneous Magnetic Fields -- 4.4.1.1 Transformation from One Curvilinear Frame to Another -- 4.4.1.2 Transformations Between Curvilinear and Local Frames at a Fixed Point on the Particle Trajectory -- 4.4.1.3 Transformations Between Global Cartesian and Local Frames -- 4.4.2 Inhomogeneous Magnetic Fields -- 4.5 Material Effects -- 4.5.1 Multiple Scattering -- 4.5.1.1 The Distribution of the Scattering Angle -- 4.5.1.2 Multiple Scattering in Track Propagation -- 4.5.2 Energy Loss by Ionization -- 4.5.2.1 Mean Energy Loss -- 4.5.2.2 Ionization Energy Loss in Track Propagation -- 4.5.3 Energy Loss by Bremsstrahlung -- 4.5.3.1 Mean and Distribution of the Energy Loss -- 4.5.3.2 Approximation by Gaussian Mixtures -- References -- 5 Track Finding -- 5.1 Basic Techniques -- 5.1.1 Conformal Transformation -- 5.1.2 Hough Transform -- 5.1.3 Artificial Retina -- 5.1.4 Legendre Transform -- 5.1.5 Cellular Automaton -- 5.1.6 Neural Networks -- 5.1.6.1 Hopfield Network -- 5.1.6.2 Recurrent Neural Network -- 5.1.6.3 Graph Neural Network -- 5.1.7 Track Following and the Combinatorial Kalman Filter -- 5.1.8 Pattern Matching -- 5.2 Online Track Finding -- 5.2.1 CDF Vertex Trigger -- 5.2.2 ATLAS Fast Tracker -- 5.2.3 CMS Track Trigger -- 5.2.3.1 Time Multiplexing -- 5.2.3.2 Pattern Matching -- 5.3 Candidate Selection -- References -- 6 Track Fitting -- 6.1 Least-Squares Fitting -- 6.1.1 Least-Squares Regression -- 6.1.2 Extended Kalman Filter -- 6.1.3 Regression with Breakpoints -- 6.1.4 General Broken Lines -- 6.1.5 Triplet Fit6.1.6 Fast Track Fit by Affine Transformation -- 6.2 Robust and Adaptive Fitting -- 6.2.1 Robust Regression -- 6.2.2 Deterministic Annealing Filter -- 6.2.3 Gaussian-Sum Filter -- 6.3 Linear Approaches to Circle and Helix Fitting -- 6.3.1 Conformal Mapping Method -- 6.3.2 Chernov and Ososkov's Method -- 6.3.3 Karimäki's Method -- 6.3.4 Riemann Fit -- 6.3.5 Helix Fitting -- 6.4 Track Quality -- 6.4.1 Testing the Track Hypothesis -- 6.4.2 Detection of Outliers -- 6.4.3 Kink Finding -- References -- Part III Vertex Reconstruction -- 7 Vertex Finding -- 7.1 Introduction -- 7.2 Primary Vertex Finding in 1D -- 7.2.1 Divisive Clustering -- 7.2.2 Model-Based Clustering -- 7.2.3 EM Algorithm with Deterministic Annealing -- 7.2.4 Clustering by Deterministic Annealing -- 7.3 Primary Vertex Finding in 3D -- 7.3.1 Preclustering -- 7.3.2 Greedy Clustering -- 7.3.3 Iterated Estimators -- 7.3.4 Topological Vertex Finder -- 7.3.5 Medical Imaging Vertexer -- References -- 8 Vertex Fitting -- 8.1 Least-Squares Fitting -- 8.1.1 Straight Tracks -- 8.1.1.1 Exact Fit -- 8.1.1.2 Simplified Fit -- 8.1.2 Curved Tracks -- 8.1.2.1 Nonlinear Regression -- 8.1.2.2 Extended Kalman Filter -- 8.1.2.3 Fit with Perigee Parameters -- 8.2 Robust and Adaptive Vertex Fitting -- 8.2.1 Vertex Fit with M-Estimator -- 8.2.2 Adaptive Vertex Fit with Annealing -- 8.2.3 Vertex Quality -- 8.3 Kinematic Fit -- References -- 9 Secondary Vertex Reconstruction -- 9.1 Introduction -- 9.2 Decays of Short-Lived Particles -- 9.3 Decays of Long-Lived Particles -- 9.4 Photon Conversions -- 9.5 Hadronic Interactions -- References -- Part IV Case Studies -- 10 LHC Experiments -- 10.1 ALICE -- 10.2 ATLAS -- 10.3 CMS -- 10.4 LHCb -- References -- 11 Belle II and CBM -- 11.1 Belle II -- 11.2 CBM -- References -- A Jacobians of the Parameter Transformations -- Transformation from One Curvilinear Frame to AnotherTransformations Between a Local Frame and the Curvilinear Frame -- Transformations Between the Intermediate Cartesian Frame and the Local Frame -- B Regularization of the Kinematic Fit -- Reference -- C Software -- FairRoot -- ACTS: A Common Tracking Software -- GBL: General Broken Lines -- GENFIT -- RAVE -- References -- Glossary and Abbreviations -- IndexDescription based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
A Deterministic Annealing PMHT Algorithm with an Application to Particle Tracking
Get PDFWe introduce the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementat- ion of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT). We introduce the Probabilistic Multi-Hypot- hesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementation of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT)
Propagation of Covariance Matrices of Track Parameters in Homogeneous Magnetic Fields in CMS
No full textIn this paper, a set of Jacobians used for the propagation of track parameter covariance matrices in homogeneous magnetic fields in CMS is derived. Most of the presented formulas have been in widespread use in the high-energy physics community for many years, but have until now only existed in unpublished notes. Very precise, purely numerical schemes for calculating the same derivatives are also presented and used as a baseline for evaluating the correctness of the analytical terms
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