Objektive und reproduzierbare Gefügeklassifizierung niedriglegierter Stähle

Ziel der vorliegenden Arbeit ist die Entwicklung einer objektiven und reproduzierbaren Gefügeklassifizierung niedriglegierter Stähle. Hierfür wird mit den in der Informatik zur Verfügung stehenden Methoden des maschinellen Lernens ein Arbeitsablauf für eine Klassifizierung der drei Gefügebestandteile Perlit, Bainit und Martensit erarbeitet. Als Grundlage für das Klassifizierungsmodell wird das Stützvektorverfahren (Support Vector Machine, SVM) genutzt, welches auf einen Merkmalsdatensatz der drei Klassen angewendet wurde. Für den Aufbau der Datenbank werden verschiedene Gefügemerkmale aus korrelativen Licht- und Elektronenmikroskopaufnahmen verwendet. Die Merkmalsdatenbank beinhaltet form- und größenbeschreibende Parameter sowie pixelbasierte Merkmale, die aus der Bildtextur der Mikroskopaufnahmen extrahiert werden. Der Einfluss der Datenvorverarbeitung und -aufteilung auf die Klassifizierungsergebnisse werden untersucht. Neben dem Aufbau eines validen Klassifizierungsprozesses liegt der Fokus auf der Weiterentwicklung und Identifizierung der für die Klassifizierung entscheidenden, signifikanten Gefügemerkmale. Für die aufgebaute Datenbasis können Klassifizierungsgenauigkeiten von bis zu 97 % für die vordefinierten Klassen erreicht werden. Die Methodik des vorgestellten Ansatzes der Gefügeklassifizierung kann im Bereich der Stahlwerkstoffe erweitert und auf andere Werkstoffklassen übertragen werden.The aim of this thesis is to develop an objective and reproducible microstructure classification of low-alloy steels. For this purpose, a workflow for the classification of the three microstructural constituents pearlite, bainite and martensite is established using the methods of machine learning available in computer science. The classification model is based on the support vector machine (SVM), which has been applied to a feature dataset of these three classes. To build up the database, various microstructural features extracted from correlative light and electron microscope images are used. The feature database contains shape and size describing parameters as well as pixelbased features, which are extracted from the image texture of the microscope images. The influence of data pre-processing and data splitting on classification results is investigated. In addition to the design of a valid classification process, the focus is on the development and identification of the significant microstructural features which are relevant for the classification. It is possible to achieve classification accuracies of up to 97 % for the predefined classes using the generated database. The methodology of the approach presented can be extended in the field of steel materials and be transferred to other material classes

Numerical simulation of dual-phase steel based on real and virtual three-dimensional microstructures

Dual-phase steel shows a strong connection between its microstructure and its mechanical properties. This structure–property correlation is caused by the composition of the microstructure of a soft ferritic matrix with embedded hard martensite areas, leading to a simultaneous increase in strength and ductility. As a result, dual-phase steels are widely used especially for strength-relevant and energy-absorbing sheet metal structures. However, their use as heavy plate steel is also desirable. Therefore, a better understanding of the structure–property correlation is of great interest. Microstructure-based simulation is essential for a realistic simulation of the mechanical properties of dual-phase steel. This paper describes the entire process route of such a simulation, from the extraction of the microstructure by 3D tomography and the determination of the properties of the individual phases by nanoindentation, to the implementation of a simulation model and its validation by experiments. In addition to simulations based on real microstructures, simulations based on virtual microstructures are also of great importance. Thus, a model for the generation of virtual microstructures is presented, allowing for the same statistical properties as real microstructures. With the help of these structures and the aforementioned simulation model, it is then possible to predict the mechanical properties of a dual-phase steel, whose three-dimensional (3D) microstructure is not yet known with high accuracy. This will enable future investigations of new dual-phase steel microstructures within a virtual laboratory even before their production

RVE-size Estimation and Efficient Microstructure-based Simulation of Dual-Phase Steel

Dual-phase steel shows a pronounced structure-property correlation, caused by its internal structure consisting of asoft ferrite matrix and embedded hard martensite regions. Due to its high strength combined with high ductility, dual-phasesteel is particularly suitable for energy-absorbing and strength-relevant sheet metal applications, but its use as heavy plate isalso desirable. Due to the complex microstructure, microstructure-based simulation is essential for a realistic simulation of themechanical properties of dual-phase steel. This paper describes two important points for the microstructure-based simulation ofdual-phase steel. First a method for the straightforward experimental estimation of the RVE size based on hardness measurementsprior to tomography preparation is presented and evaluated. Secondly, a method for the efficient meshing of these microstructures,based on material definition at the integration points of a finite element model, is developed

Search for Physics beyond the Standard Model in Events with Overlapping Photons and Jets

Results are reported from a search for new particles that decay into a photon and two gluons, in events with jets. Novel jet substructure techniques are developed that allow photons to be identified in an environment densely populated with hadrons. The analyzed proton-proton collision data were collected by the CMS experiment at the LHC, in 2016 at root s = 13 TeV, and correspond to an integrated luminosity of 35.9 fb(-1). The spectra of total transverse hadronic energy of candidate events are examined for deviations from the standard model predictions. No statistically significant excess is observed over the expected background. The first cross section limits on new physics processes resulting in such events are set. The results are interpreted as upper limits on the rate of gluino pair production, utilizing a simplified stealth supersymmetry model. The excluded gluino masses extend up to 1.7 TeV, for a neutralino mass of 200 GeV and exceed previous mass constraints set by analyses targeting events with isolated photons.Peer reviewe

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

Calibration of the CMS hadron calorimeters using proton-proton collision data at root s=13 TeV

Methods are presented for calibrating the hadron calorimeter system of theCMSetector at the LHC. The hadron calorimeters of the CMS experiment are sampling calorimeters of brass and scintillator, and are in the form of one central detector and two endcaps. These calorimeters cover pseudorapidities vertical bar eta vertical bar ee data. The energy scale of the outer calorimeters has been determined with test beam data and is confirmed through data with high transverse momentum jets. In this paper, we present the details of the calibration methods and accuracy.Peer reviewe

Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

Peer reviewe

An embedding technique to determine ττ backgrounds in proton-proton collision data

An embedding technique is presented to estimate standard model tau tau backgrounds from data with minimal simulation input. In the data, the muons are removed from reconstructed mu mu events and replaced with simulated tau leptons with the same kinematic properties. In this way, a set of hybrid events is obtained that does not rely on simulation except for the decay of the tau leptons. The challenges in describing the underlying event or the production of associated jets in the simulation are avoided. The technique described in this paper was developed for CMS. Its validation and the inherent uncertainties are also discussed. The demonstration of the performance of the technique is based on a sample of proton-proton collisions collected by CMS in 2017 at root s = 13 TeV corresponding to an integrated luminosity of 41.5 fb(-1).Peer reviewe

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe