Regionalism in GDR-Modernism of the 1960s and 1970s

The widespread narrative that all GDR-Metropolises were overwritten by a soul- and faceless socialist variant of post-war modernism must be questioned at least regarding some cities. Although whole streets were torn down in the 1960s and 1970s and history was only partially appreciated, there can be found a series of modernist buildings respecting local traditions. Thus, regionalisms express themselves in traditional building materials – in the early 1960s most prominently in the Northeastern City of Rostock, whose brick-faced postwar buildings in the centre were recently categorized as ‘Nordmoderne’ (‘northern modernism’). In the late 1960s and early 1970s, meanwhile, attempts have been made in Potsdam to adapt new modern structures to existing baroque and classicist buildings by materials, facade colours or vertical subdivisions of facades – in order to merge new and old buildings into a ‘harmonic’ unity. Regarding the development of modernism in East Germany, the regionalisms mentioned above seem to have different roots. As the Haus der Schiffahrt in Rostock’s Lange Straße could be explained as late successor of the 1950s Stalinist doctrin of the ‘National Tradition’, buildings such as the Institut für Lehrerbildung or Staudenhof in Potsdam seem to be efforts to avoid increasing monotony of east-modern architecture.info:eu-repo/semantics/publishedVersio

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out

MetaCook: FAIR Vocabularies Cookbook

One of the prerequisites for FAIR data publication is the use of FAIR vocabularies. Currently, tools for the collaborative composition of such vocabularies are missing. For this reason, a universal manual and software for user-friendly vocabulary assembly is being composed in the HMC-funded MetaCook project. The project includes 4 separate test cases from 4 labs across KIT and Hereon, which will help strengthen the software\u27s universality and applicability to various domains. The components described in MetaCook will be implemented in the form of multiple software tools. The first one, a Python-based web application called VocPopuli, is the entry point for domain experts. The software, whose first version is being developed at the time of writing, enables the collaborative definition, and editing of metadata terms. Additionally, it annotates each term, as well as the entire vocabulary, with the help of the PROV Data Model (PROV-DM) - a schema used to describe the provenance of a given object. Finally, it assigns a unique ID to each term in the vocabulary, as well as a hash-based ID the vocabulary itself. The second software tool will facilitate the transformation of the vocabularies developed with the help of VocPopuli into ontologies. It will handle two distinct use cases – the from-scratch conversion of vocabularies into ontologies, and the augmentation of existing ontologies with the terms from a given thesaurus. Both software tools will be used by two semi-overlapping user groups: domain experts will input, edit, and discuss vocabulary terms in their area of interest, while vocabulary and ontology administrators will oversee the vocabulary creation, and ontology transformation. Both the controlled vocabularies and the corresponding ontologies offer the possibility to enrich data documented in Electronic Laboratory Notebooks (ELNs). As the simplest solution, terms used within the ELN are linked to the IDs of the related vocabulary and ontology for an unambiguous definition. Additionally, an export of the defined schemes can be used to automatically create a structured form in the ELNs for documenting the described processes. The output from the developed tools will be exemplarily integrated into the ELNs Herbie and Kadi4Mat

Blick zurück nach vorn. Architektur und Stadtplanung in der DDR

Der aktualisierten Sichtweise auf einen widerspruchsvollen Architekturprozess war im November 2014 in Berlin eine Tagung des Arbeitskreises „Kunst in der DDR“ gewidmet. Aus den dort vorgestellten Beiträgen setzt sich die vorliegende Sammlung von sieben Aufsätzen zusammen. Es ist nicht ungewöhnlich, das Bauschaffen in einen umfassenderen Kunstdiskurs einzubetten, da es nicht zuletzt um Ausdruckswerte und die kreative Leistung von Individuen geht, doch stellen sich hier Fragen der Ökonomie, der Technik und des Gebrauchswerts in ganz anderer Dimension

Das andere Potsdam. DDR-Architekturführer. 26 Bauten und Ensembles aus den Jahren 1949-1990

Das andere Potsdam. DDR-Architekturführer. 26 Bauten und Ensembles aus den Jahren 1949-1990, hg. v. Christian Klusemann, Berlin: Vergangenheitsverlag 2016 (294 S., zahlreiche farbige und s/w-Abbildungen), ISBN: 978-3864082009 - - - Rezensiert von Martin Bredenbec

Fatigue crack propagation in AA5083 structures additively manufactured via multi-layer friction surfacing

Multi-layer friction surfacing (MLFS) is a layer deposition technique that allows building structures from metals in solid state. As approach for additive manufacturing, the re-heating during subsequent deposition processes is significantly lower compared to fusion-based techniques. Available research work presents promising properties of MLFS structures from aluminum alloys, reporting no significant directional dependency in terms of tensile strength. The present study focuses on the fatigue crack propagation behavior and the role of layer-to-substrate (LTS) as well as layer-to-layer (LTL) interfaces. Compact tension specimens were extracted in different orientations from the MLFS stacks built from AA5083. The crack propagation parallel and perpendicular to the LTL interfaces as well as from the substrate material across LTS interface into the MLFS deposited material was investigated. The results show that LTL interfaces play no significant role for the crack propagation, i.e. specimens with LTL interfaces perpendicular and parallel to the crack presented no significant differences in terms of their fatigue crack propagation behavior. The specimens where the crack propagated from the substrate material across the LTS interface into the MLFS deposited material showed higher fatigue life than the specimens with crack propagation in the MLFS deposited material only. Crack retardation can be observed as long as the crack propagates within the substrate material, which is associated with compressive residual stresses introduced in the substrate during the layer deposition process

A review of the application of machine learning and data mining approaches in continuum materials mechanics

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out

Rapid grain refinement and compositional homogenization in a cast binary Cu50Ni alloy achieved by friction stir processing

Friction stir processing (FSP) has been increasingly adopted for joining and processing materials in automotive, aerospace, and industrial construction. During FSP, a dynamic competition between high-speed shear deformation and deformation-induced heating brings about a complex competition between multiple dynamic microstructural evolution mechanisms making it difficult to predict the microstructural evolution pathway. Hence, improved understanding of microstructural evolution mechanisms during FSP can be beneficial for continued growth in the adoption of FSP for demanding applications of future. Towards this goal, this study uses a model binary Cu – 50 at.% Ni alloy to clarify the effect of single and double pass FSP on the microstructural evolution of a coarse grained and compositionally heterogeneous cast microstructure. High energy synchrotron X-ray diffraction, electron backscatter diffraction, and nanoindentation are used to clarify the microstructural evolution due to FSP. The process of compositional homogenization of as-cast segregations is studied by energy dispersive spectroscopy and atom probe tomography. Our results show that a single fast FSP pass at 30 mm.s−1 produces a 100 μm deep layer of submicrometric and hall-petch hardened CuNi grains. The initial cast compositional heterogeneities in a micrometric scale is rapidly transformed to nano-sized domains, mainly confined at grain boundaries. Double pass FSP increases the penetration depth of the processed layer and leads to a 2.9 times grain growth relative to single pass FSP. Grain fragmentation, discontinuous dynamic recrystallization, grain growth, and twinning mechanisms are discussed. These results highlight the value of FSP for ultrafast grain refinement and compositional homogenization of cast alloys