Open Access

Forming complex parts out of high and ultra-high strength aluminium alloys has proved to be more challenging in comparison to the currently used deep drawing steels. Nevertheless, aluminium alloys show a limited formability in contrast with, for example, deep drawing steels. Novel processes like Warm-forming, W-Temper or Hotforming, offer the potential to produce light and highly integrated one-piece components from such aluminium alloys at elevated temperatures. When considering aluminium alloys of the 7000 group, which can reach strength values (UTS) of about 600 MPa, crash components such as side impact bars would offer a suitable field of application. Forming at elevated temperatures, in particular with the Hotforming process, offers high potential in the production of complex structural components on the one hand and in the use of existing press hardening equipment on the other. To date, the material behaviour of aluminium alloys in the 7000 group, applied in such processes and in the later final state after forming, is not sufficiently known. Therefore, in this study, systematic investigations on the formability and the final strength during and after forming at elevated temperature of the EN AW-7075 aluminium wrought alloy from different suppliers are conducted. In general, material- and damage/ failure models were created and implemented into simulation in order to make predictions. Characterisation of the plastic material properties on the basis of various tensile specimens as shear-, notched-, tensile- and Erichsen tests are carried out to adapt the complex material- and failure models such as Barlat YLD2000 and GISSMO to the experimental values using a parameter optimisation. These were made for the material conditions during forming, i.e. after solution heat treatment, the final condition after artificial ageing at 180°C for 20 minutes, which corresponds to the cathodic dip coating, and the T6 condition, which is the highest strength condition. To evaluate a suitable friction coefficient for high temperature forming processes, anti-friction agents are screened, and the potential applicability evaluated by strip-drawing tests. Thereby, using an analytical relationship, friction coefficients are determined at room temperature and 180°C, which are used as corresponding friction model for the finite element forming simulation. Crash simulations using the nonlinear finite element method (FEM) of side impact protection beams are used to demonstrate the weight saving potential of high and ultra-high strength aluminium alloys compared to a beam made of press hardened steel. A weight saving of about 20 % could be achieved with the same crash performance. This can be significantly increased to around 30 % - 40 % by using local reinforcements such as CFRP or GFRP (carbon/ glass fibre reinforced plastic) patch. For this reason, a novel process was developed which is based on the conventional Hotforming process with an integrated thermal direct joining step called “Extended Hotforming”. Subsequently, a heatable forming tool for the production of a serial like sheet metal side impact beam was developed to validate the finite element simulation and to demonstrate the potential of the forming processes at elevated temperatures for aluminium sheet metal components.

As the amount of software source code increases, manual approaches for documentation or detection of software design patterns in source code become inefficient relative to the value. Furthermore, typical automatic pattern detection tools are limited to a single programming language. To address this, our Design Pattern Detection using Machine Learning (DPDML) offers a generalized and programming language agnostic approach for automated design pattern detection based on machine learning (ML). The focus of our evaluation was on ensuring DPDML can reasonably detect one design pattern in the structural, creational, and behavioral category for two popular programming languages (Java and C#). 60 unique Java and C# code projects were used to train the artificial neural network (ANN) and 15 projects were then used to test pattern detection. The results show the feasibility and potential for pursuing an ANN approach for automated design pattern detection.

SARS-CoV-2 bestimmt maßgeblich unseren Alltag. Seit einiger Zeit ständiger Wegbegleiter im öffentlichen Raum und teilweise auch modisches Accessoire: Der Mund-Nasenschutz. Im Rahmen einer Mixed-Method-Studie geht das Aalener Institut für Unternehmensführung (AAUF) der privaten Nutzung von Mund-Nasenschutz im öffentlichen Raum (Einzelhandel und ÖPNV) auf den Grund. Dafür wurden im Zeitraum vom 25.05.2020 bis 13.06.2020 insgesamt 2.393 Personen in einer Online-Erhebung befragt und die gewonnenen Erkenntnisse um persönliche Interviews (433 Probanden) und Beobachtungen (691 Probanden) vor diversen Supermärkten und Einkaufszentren im süddeutschen Raum ergänzt. Dieser Forschungsbericht enthält die Ergebnisse der während des Zeitraums vom 02.06.2020 bis 06.06.2020 durchgeführten Interviews.

Die vorliegende Studie beschäftigt sich mit der Hygiene-Compliance in Krankenhäusern. Zur Einhaltung von Regeln der Hände- und Flächendesinfektion wurden hierzu Entscheider von Krankenhäusern in Deutschland, Österreich und der Schweiz befragt. In der Studie zeigt sich eine starke Sensibilisierung für das Thema Compliance. Hier ist hinzuzufügen, dass die Studie vor der Corona-Pandemie durchgeführt wurde, und bereits zu diesem Zeitpunkt gab es eine Aufmerksamkeit für das Thema.

Die vorliegende Untersuchung beschäftigt sich mit der Umsetzung und Implementierung von Compliance-Management in deutschen Unternehmen. Dazu wurden 142 Unternehmen anhand einer elektronischen Umfrage befragt. Der Fragebogen enthält 32 Fragen, diese sind in die drei Abschnitte „allgemeine Unternehmensfragen“, „Fragen zum Compliance Management“ und „demographische Daten“ unterteilt. Die Umfrage fokussiert sich hierbei besonders auf Fragestellungen hinsichtlich der organisatorischen Eingliederung, Funktionsweise, Aufgaben und Instrumente sowie der Bewertung des Compliance-Managements innerhalb der Unternehmen.