An AI-driven design method as basis for teaming

The product development process could benefit from a synergistic human-machine teaming, potentially shortening product development cycles and improving product performance and sustainability. However, there is a lack of available methods to achieve this goal. A technical product has to satisfy numerous requirements. Due to the variety and complexity of these requirements, the design process is challenging for human engineers. While engineers are supported by various tools (e.g. FEM) for analyzing product properties, tools for computer-aided synthesis of product properties considering the corresponding requirements are still only available in exceptional cases. However, such synthesis capabilities are necessary to qualify a computer-aided tool for productive teaming with engineers. Special methods based on artificial intelligence show a high potential for general computer-aided synthesis methods. This contribution presents an innovative approach in this direction based on topology optimization techniques

An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data

Topology optimization is widely used by engineers during the initial product development process to get a first possible geometry design. The state-of-the-art is the iterative calculation, which requires both time and computational power. Some newly developed methods use artificial intelligence to accelerate the topology optimization. These require conventionally pre-optimized data and therefore are dependent on the quality and number of available data. This paper proposes an AI-assisted design method for topology optimization, which does not require pre-optimized data. The designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling (the volume percentage filled by material) as input data. In the training phase, geometries generated on the basis of random input data are evaluated with respect to given criteria. The results of those evaluations flow into an objective function which is minimized by adapting the predictor's parameters. After the training is completed, the presented AI-assisted design procedure supplies geometries which are similar to the ones generated by conventional topology optimizers, but requires a small fraction of the computational effort required by those algorithms. We anticipate our paper to be a starting point for AI-based methods that requires data, that is hard to compute or not available

Simulation von Passfederverbindungen mittels elastisch-plastischer Materialmodelle

Zunehmendes Downsizing und der Trend zum Leichtbau bei Welle-Nabe-Verbindungen erfordern eine exakte Beschreibung des Systemverhaltens. Elastische Simulationen erfordern im Post-processing die Analyse komplexer Zusammenhänge, welche oftmals nur empirisch begründet sind. Elastisch-Plastische Materialmodelle geben die Möglichkeit Stütz- und Setzeffekte von Passfederverbindungen bereits während der Simulation abzubilden. Die vorliegende Arbeit wendet elastisch-plastische Materialmodelle auf Passfederverbindungen an, um auftretende Versagensmechanismen zu beschreiben.Downsizing and the trend to lightweight design ofshaft-hub-connections need an accurate description of the behaviour of the system. In post-processing, elastic simulations require a complex analysis based on empiric formula. Using elastic-plastic material models enable the possibility to respect support and set effects of feather key connections within the simulation. The current paper applies elastic-plastic material models to feather key connections in order to describe occurring failure mechanisms

A selection of lessons learned from phase C/D of CubeSat projects of the Fly Your Satellite! programme

Fly Your Satellite!” (FYS) is a recurring programme part of ESA Academy’s portfolio of “hands-on” activities. The programme was established to support University student teams in the development of their own CubeSat missions and aims at transferring knowledge and experience from ESA specialists to students. Selected teams are guided through project reviews and supervised through design consolidation and verification activities, conducted according to ESA professional practice and standards, tailored to fit the scope of university CubeSat projects. As part of the educational goal of the programme, a systematic effort of capturing, discussing and contextualising difficulties, mistakes, and anomalies in general, is carried out. From this effort, the participating students benefit from a unique framework where lessons learned from one project can be transferred to other ones. This exercise is blended with the “regular” transfer of knowledge from the ESA professionals that support the programme and occurs both concurrently (lessons learned from current cycles) and from previous projects (lessons learned from previous cycles). This paper reports a revised and updated collection of lessons learned during phase C/D of the FYS CubeSat projects, in particular the projects now participating in the 2nd cycle (FYS2). At the same time potential changes and mitigating approaches are discussed. Particular focus is given to lessons learned from issues which arose in hardware development activities, as well as from planning and execution of system-level assembly, integration, and verification (AIV) activities. This approach is taken since first-time developers tend to underestimate the number of issues arising when their design is translated from documentation and models into real hardware. In general, it has been observed that many of these issues typically arise from lack of (space) project management experience of the student teams, or from the lack of resources which prevent the application of standard/established methodologies to small satellite/educational project

Simulation eines punktbelasteten Wälzlageraußenrings zur Untersuchung der Schlupfzustände in der Fuge

Aufgrund ökonomischer Forderung und dem Trend zum Leichtbau werden Lagergehäusen und Anschlusskonstruktionen zunehmend dünnwandiger gestaltet. Die damit einhergehenden Verformungen begünstigen die Relativbewegungen zwischen Gehäuse/Welle. Die vorliegende Arbeit untersucht mittels FE-Simulation die Schlupfzustände in der Fuge die aus den Verformungen des dünnwandigen Gehäuses und Anschlusskonstruktionen resultieren.Due to economic demands and the trend towards lightweight construction, bearing housings are becoming increasingly thin-walled. As a result, the bearing housing become more compliant. The associated deformations favour the relative movements between housing/shaft. The present work investigates the slip conditions in the joint resulting from the deformations of the thin-walled housing by means of FE simulation

Observations upon the structure and normal contents of the ductus and saccus endolymphaticus in the Guinea-pig (Cavia cobaya)

No Abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49600/1/1000390102_ftp.pd