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Aufsatz
Effect of Grain Statistics on Micromechanical Modeling: The Example of Additively Manufactured Materials Examined by Electron Backscatter Diffraction
(2020-02-21)
Micromechanical modeling is one of the prominent numerical tools for the prediction of mechanical properties and the understanding of deformation mechanisms of metals. As input parameters, it uses data obtained from microstructure characterization techniques, among which the electron backscatter diffraction (EBSD) technique allows us to understand the nature of microstructural features, that are usually described by statistics. Because of these advantages, the EBSD dataset is widely used for synthetic microstructure ...
Aufsatz
Effect of Tool Temperature on Mechanical Properties and Microstructure of Thermo-Mechanically Processed AA6082 and AA7075 Aluminum Alloys
(2020-06-08)
The present work focuses on the effect of thermo-mechanical processing on the mechanical properties and microstructural evolution of AA6082 and AA7075 aluminum alloys using a novel forming process, i. e. integrating solution heat treatment, hot forming and tool quenching. Different tool temperatures ranging from 24 °C to 350 °C were applied to investigate their influence on mechanical strength and ductility. By using various tool temperatures, this study aims to provide insights needed for tailoring the mechanical ...
Aufsatz
Effects of Aging under Stress on Mechanical Properties and Microstructure of EN AW 7075 Alloy
(2021-07-20)
In the present study, microstructural and mechanical properties of EN AW 7075 following stress-aging were assessed. For this purpose, properties of stress-aged samples were compared with values obtained for conventionally aged counterparts. It is revealed that the strength and hardness of EN AW 7075 can be increased by the presence of external stresses during aging. Precipitation kinetics were found to be accelerated. The effects of conventional and stress-aging on the microstructure were analyzed using synergetic ...
Aufsatz
Effect of Friction Stir Processing on Microstructural, Mechanical, and Corrosion Properties of Al-Si12 Additive Manufactured Components
(2020-01-03)
Additive manufacturing (AM) is an advanced manufacturing process that provides the opportunity to build geometrically complex and highly individualized lightweight structures. Despite its many advantages, additively manufactured components suffer from poor surface quality. To
locally improve the surface quality and homogenize the microstructure, friction stir processing (FSP) technique was applied on Al-Si12 components produced by selective laser melting (SLM) using two different working media. The effect of FSP on ...
Aufsatz
Influence of complex geometries on the properties of laser-hardened surfaces
(2020-04-25)
Laser surface hardening provides for many advantages in terms of flexible production due to very localized and controlled energy input into the material. Laser processing offers the possibility to treat surfaces in order to locally strengthen the areas that are prone to fatigue cracking. It is well known that laser energy absorption depends on many parameters, e.g., the surface structure and the surface orientation. The incident angle of the laser beam plays a key role in this regard. When complex geometries like ...
Aufsatz
Consequences of Deep Rolling at Elevated Temperature on Near-Surface and Fatigue Properties of High-Manganese TWIP Steel X40MnCrAl19-2
(2021-11-05)
Due to pronounced work-hardening induced by the complex interplay of deformation mechanisms such as dislocation slip, twinning and/or martensitic phase transformation, high-manganese steels represent a class of materials well-suited for mechanical surface treatment. In the present study, the fatigue behavior of a high-mangsanese steel showing twinning-induced plasticity (TWIP) effect at room temperature (RT) was investigated after deep rolling at 550 °C. Results are compared to a former study discussing the behavior ...
Masterarbeit
Vergleich & Anpassung zweier Strategien zur Anomalieerkennung in Lastgängen basierend auf Verfahren aus den Bereichen Machine-Learning und Statistik
(2020-11)
In dieser Masterarbeit werden zwei Strategien zur Anomalieerkennung in Lastgängen ausgewertet. Dazu nutzt Strategie 1 das künstliche neuronale Netzwerk LSTM (Long Short-Term Memory) mit Datenzeitraum von einem Monat (1M) bzw. drei Monaten (3M) trainiert und Strategie 2 das Glättungsverfahren PEWMA (Probalistic Exponential Weighted Moving Average) zur Schätzung des zu untersuchenden Lastgangmonats. Durch den Vergleich mit Originallastgangdaten werden Residuen bzw. summierte Residuen der Sequenzlängen zwei, vier, sechs ...
Aufsatz
Excellent superelasticity in a Co-Ni-Ga high-temperature shape memory alloy processed by directed energy deposition
(2020-04-30)
A Co-Ni-Ga high-temperature shape memory alloy has been additively manufactured by directed energy deposition. Due to the highly anisotropic microstructure, i.e. columnar grains featuring a strong near-⟨001⟩ texture in build direction, the as-built material is characterized by a very low degree of constraints and, thus, shows excellent superelasticity without conducting a post-process heat treatment. As characterized by in situ deformation testing and post-mortem microstructural analysis, additive manufacturing ...
Aufsatz
Mechanics of colloidal supraparticles under compression
(2021-10-13)
Colloidal supraparticles are finite, spherical assemblies of many primary particles. To take advantage of their emergent functionalities, such supraparticles must retain their structural integrity. Here, we investigate their size-dependent mechanical properties via nanoindentation. We find that the deformation resistance inversely scales with the primary particle diameter, while the work of deformation is dependent on the supraparticle diameter. We adopt the Griffith theory to such particulate systems to provide a ...
Aufsatz
On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning
(2020-10-16)
In this article, two data-driven modeling approaches are investigated, which allow an explicit modeling of uncertainty. For this purpose, parametric Takagi-Sugeno multi-models with bounded-error parameter estimation and nonparametric Gaussian process regression are applied and compared. These models can for instance be used for robust model-based control design. As an application, the prediction of residual stresses during hard turning depending on the machining parameters and the initial hardness is considered.