Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2022Generalization of the Weibull probabilistic compatible model to assess fatigue data into three domains: LCF, HCF and VHCF24citations
  • 2016Modified CCS fatigue crack growth model for the AA2019-T851 based on plasticity-induced crack-closure46citations
  • 2010Analysis of constant and variable amplitude strain-life data using a novel probabilistic Weibull regression modelcitations

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Castillo, E.
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Correia, Jafo
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De Jesus, Amp
3 / 92 shared
Blason, S.
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Arcari, A.
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Apetre, N.
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Moreira, Pmgp
1 / 19 shared
Calvente, M.
1 / 1 shared
Pinto, H.
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Co-Authors (by relevance)

  • Castillo, E.
  • Correia, Jafo
  • De Jesus, Amp
  • Blason, S.
  • Arcari, A.
  • Apetre, N.
  • Moreira, Pmgp
  • Calvente, M.
  • Pinto, H.
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document

Analysis of constant and variable amplitude strain-life data using a novel probabilistic Weibull regression model

  • Pinto, H.
  • Canteli, Af
  • De Jesus, Amp
Abstract

The relation between the total strain amplitude and the fatigue life measured in cycles is usually given as strain-life curves based on the former proposals of Basquin for the elastic strain-life and Coffin-Manson for the plastic strain-life. In this paper, a novel Weibull regression model, based on an existing well established Weibull model for the statistical assessment of stress-life fatigue data, is proposed for the probabilistic definition of the strain-life field. This approach arises from sound statistical and physical assumptions and not from an empirical proposal insufficiently supported, provides an analytical probabilistic definition of the whole strain-life field as quantile curves both in the low-cycle and high-cycle fatigue regions, deals directly with the total strain without the need of separating its elastic and plastic strain components, permit dealing with run-outs, and can be applied for probabilistic lifetime prediction using damage accumulation. The parameters of the model can be estimated using different well established methods proposed in the fatigue literature, in particular, the maximum likelihood and the two-stage methods. In this work, the proposed model is applied to analyze fatigue data, available for a pressure vessel material - the P355NL1 steel, consisting of constant amplitude, block and spectrum loading, applied to smooth specimens, previously obtained and published by authors. A new scheme to deal with variable amplitude loading in the background of the proposed regression strain-life Weibull model is described. The possibility to indentify the model constants using both constant amplitude and two-block loading data is discussed. It is demonstrated that the proposed probabilistic model is able to correlate the constant amplitude strain-life data. Furthermore, it can be used to correlate the variable amplitude fatigue data if the model constants are derived from two block loading data. The proposed probabilistic regression model is suitable for reliability analysis of notched details in the framework of the local approaches. Copyright © 2009 by ASME.

Topics
  • impedance spectroscopy
  • polymer
  • steel
  • fatigue