Symposium

M03: Computer Aided Reliability in Mechanical Engineering

Belongs to:
TopicM: Modelling and Simulation

Understanding and describing the mechanical behaviour and the damage evolution of materials used for highly-loaded components is a key requirement for a reliable use in general. In this context, a lot of research effort has been spent to develop and refine physical and mechanism based constitutive models in order to increase the preciseness of the underlying mechanical and damage predictions. In addition, probabilistic methods have been introduced into assessment concepts recently in order to describe both, the loading and the material scatter and thus the model input parameters by means of optimised probabilistic approaches. Lately, also pure data-driven approaches like Machine Learning come into play: on the one hand, to search for cross-correlations and to increase the understanding of the material; and on the other hand, to generate surrogate or so called grey-box models to train and provide very efficient tools for lifetime assessment in future.

Within this symposium, the role of mechanism based material models, innovative material sensor applications, novel parameter identification approaches, probabilistics and machine learning will be connect to each other and discussed together in order to summarize recent advances in the context of a future “Computer Aided Reliability in Mechanical Engineering”. The goal here is, to provide an interdisciplinary forum between material scientists, mechanical engineering experts, mathematicians and machine learning specialists to bring all these experts together at the MSE 2020 within this symposium or dedicated joint sessions respectively.


Lecture M03: Computer Aided Reliability in Mechanical Engineering
WEB Data-driven modelling workflows for crack growth under complex loading scenarios

Dr.-Ing. Michael Krämer Dr. Christian Kontermann Prof. Dr. Matthias Oechsner

Lecture M03: Computer Aided Reliability in Mechanical Engineering
WEB Parameter optimization for a physics-based model of material plastic behavior using machine learning

Dr. Evgeniya Kabliman Dr. Johannes Kronsteiner Dr. Michael Kommenda Prof. Dr. Gabriel Kronberger

Lecture M03: Computer Aided Reliability in Mechanical Engineering
WEB Towards component series production in laser powder bed fusion by melt pool control

Alexander Großmann Holger Merschroth Julian Felger Guillaume Meyer Prof. Dr. Matthias Weigold Prof. Dr. Peter Pelz Prof. Dr. Christian Mittelstedt

Poster M03: Computer Aided Reliability in Mechanical Engineering
Computer Aided Reliability by additive manufacturing of components with locally varied properties

Dr. Iliya Radulov Dr. Stefan Riegg Dr. Konstantin Skokov Lukas Schäfer Tobias Braun Prof. Dr. Oliver Gutfleisch

Lecture M03: Computer Aided Reliability in Mechanical Engineering
Computer aided detection of sensitive parameters in additive manufacturing

Sören Wenzel Dr. Elena Slomski-Vetter Prof. Dr. Tobias Melz

Lecture M03: Computer Aided Reliability in Mechanical Engineering
Data-driven surrogate models for the acceleration of two-scale problems in mechanical engineering

Dr.-Ing. Mauricio Fernández Prof. Dr. Oliver Weeger Prof. Dr. Felix Fritzen Shahed Rezaei Dr. Jaber Rezaei Mianroodi Prof. Dr. Stefanie Reese

Lecture M03: Computer Aided Reliability in Mechanical Engineering
Experimental investigation of the microstructure design in laser powder bed fusion using In625

Holger Merschroth Marius Hoffmann Prof. Dr. Matthias Weigold Johannes Geis Prof. Dr. Eckhard Kirchner

Lecture M03: Computer Aided Reliability in Mechanical Engineering
Fully automated finite element simulation and evaluation thereof as white-box elements in grey-box models

Max Benedikt Geilen Dr. Marcus klein Prof. Dr. Matthias Oechsner

Lecture M03: Computer Aided Reliability in Mechanical Engineering
Influence of the miniaturization effect on lattice structures by means of PBF with AlSi10Mg

Dipl.-Ing. Guillaume Meyer Dr. Enrico Bruder Prof. Dr. Karsten Durst Prof. Dr. Christian Mittelstedt

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