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WEB Computer Aided Reliability by additive manufacturing of components with locally varied properties

Friday (28.02.2020)
15:39 - 15:39 Poster Room
Part of:
- Poster *web*Simulation of Spinning Processes with Experimental Validation 1 Stefan Hermanns
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- Poster *web*Effective mobility of BCC dislocations in two-dimensional discrete dislocation plasticity 1 Tarun Katiyar
- Poster *web*Parallelizable electrocatalytic oxygen evolution reactions on metal phosphides 1 Seulgi Ji
- Poster *web*Computer Aided Reliability by additive manufacturing of components with locally varied properties 1 Dr. Iliya Radulov
- Poster *web*Numerical Study of Solidification in Metallic Droplet 1 Dandan Yao
- Poster TBA -
- Poster TBA -
- Poster *web*Investigation of hexagonal beryllium grain boundaries under irradiation with atomistic modelling and simulation 1 Dipl.-Ing. Thomas Le Crane
- Poster TBA -
- Poster *web*Thermodynamic assessment and modelling of interactions in liquid Ni alloys/oxide systems 1 Saverio Sitzia
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- Poster *web*HMC hub matter for as a resource for materials research in the German research landscape 1 Dr. Oonagh Mannix
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- Poster *web*Rational Design of Metal Anchored Carbon Quantum Dots as Optimal Efficient Electrocatalysts for Hydrogen production Using Machine Learning and High-throughput Experimentation 1 Yujin Chae
- Poster *web*Correlating Raman Spectra of Ibuprofen, Nicotinamide and Their Dimers 1 Milad Asgarpour Khansary
- Poster *web*High Throughput Screening for Electrochemical Nitrogen Reduction for the Ammonia Synthesis via Machine Learning 1 JaeHyoung Lim
- Poster Thermodynamic investigation of oxidation behavior of NiAl intermetallics with embedded Cr and Mo 1 Golnar Geramifard

Session -M: Modelling, Simulation, and Data
Belongs to:
Topic X: Poster Session

Quality of products is largely determined by their reliability. Accordingly, both, the predictive assessment of the reliability of components and the alignment of the design of components with reliability requirements are central issues of today as well as of future mechanical engineering [1]. This important industry will face two main challenges in the next few years:

The digital transformation along the product life cycle (LC) will bring drastic changes through new business models with new actors [2]. Today's use of digital data and processes in the product development phase by Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) represents only a small part of the upcoming transformation process: the end of this transformation is the digitization of the entire product LC.

Additive manufacturing (AM) will enable completely new process flows and products with significantly shorter innovation cycles and a higher degree of individualization, but the reliability of those products cannot yet be described to the extent required. However, AM has the potential to become a key technology for functional integration (e.g. integration of sensors and actuators). The integration of sensors will continuously and quantitatively provide information about the actual state of the component, with much greater depth of detail than previously, over the entire LC. This opens up the possibility of individual, stress-oriented component design.

Our goal is, through the production and qualification of functional materials, to develop a new material class for additive processing, which enables the production and integration of sensory capabilities. This means that components can be equipped directly with sensors in order to record stresses during the use phase and to include them in the reliability assessment. By selecting suitable process monitoring systems, inline data acquisition can be implemented. The data obtained in this way will be evaluated and related to the target values via gray box models, so that reliable process monitoring for evaluating the local component properties is made possible. Components with locally varied properties can be designed based on the knowledge obtained.

On the other hand, through process development, we will study the relationships between processing parameters and material properties. Based on this, components can then be explicitly adapted to the loads caused by locally varied properties.

Dr. Iliya Radulov
Technische Universität Darmstadt
Additional Authors:
  • Dr. Stefan Riegg
    Technische Universität Darmstadt
  • Dr. Konstantin Skokov
    Technische Universität Darmstadt
  • Lukas Schäfer
    Technische Universität Darmstadt
  • Tobias Braun
    Technische Universität Darmstadt
  • Prof. Dr. Oliver Gutfleisch
    Technische Universität Darmstadt