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ANSLC Artifitial Neural Strain Life Curves

[+] Author Affiliations
Chalid el Dsoki, Holger Hanselka, Andreas Röbig

TU-Darmstadt, Darmstadt, Germany

Heinz Kaufmann

Fraunhofer Institute for Structural Durability and System Reliability, Germany

Paper No. DETC2008-49506, pp. 179-186; 8 pages
doi:10.1115/DETC2008-49506
From:
  • ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 28th Computers and Information in Engineering Conference, Parts A and B
  • Brooklyn, New York, USA, August 3–6, 2008
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4327-7 | eISBN: 0-7918-3831-5
  • Copyright © 2008 by ASME

abstract

A durable design for linear flow split sheet components requires suitable methods and transferability criteria which are not yet available for dentritic structures. Knowledge of the cyclic material behaviour is essential for this. For this reason, the cyclic material parameters are determined as a function of the product’s properties (level of deformation, microstructure, surface finish, residual stresses) and different loading parameters. However, since the determination of the cyclic parameters is associated with considerable experimental effort and costs, a cost-effective and easy method is sought to determine these parameters. A very promising approach for this is the application of artificial neural networks (ANN) [1, 2, 3, 4, 5] since they have the ability to generate the influences on the fatigue strength from the manufacturing and environmental parameters using sensibly selected input parameters. They offer the possibility to access acquired knowledge and to thus construct a multidimensional map based on a few tests.

Copyright © 2008 by ASME

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Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

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