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Relating gigacycle fatigue to other methods in evaluating the inclusion distribution of a H13 tool steel
Karlstads universitet, Fakulteten för teknik- och naturvetenskap, Avdelningen för maskin- och materialteknik.
Karlstads universitet, Fakulteten för teknik- och naturvetenskap, Avdelningen för maskin- och materialteknik.
Karlstads universitet, Fakulteten för teknik- och naturvetenskap, Avdelningen för maskin- och materialteknik.
Karlstads universitet, Fakulteten för teknik- och naturvetenskap, Avdelningen för maskin- och materialteknik.ORCID-id: 0000-0001-6029-2613
2007 (engelsk)Inngår i: Fourth International Conference on Very High Cycle Fatigue (VHCF-4) / [ed] John E. Allison, J. Wayne Jones, James M. Larsen & Robert O. Ritchie, TMS (The Minerals, Metals & Materials Society) , 2007, s. 45-50Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Inclusions play a crucial role for the fatigue properties of high strength steel, but to find the largest inclusions by microscopy measurements large areas have to be examined.In this study ultrasonic gigacycle staircase fatigue testing has been used to find large inclusions in an H13 tool steel. The inclusions have been examined in SEM and their size distribution modeled using methods from extreme value statistics. The inclusion distribution obtained from the fatigue crack surfaces is compared to distributions acquired by microscopy study of cross sections as well as ultrasound immersion tank measurements and to the corresponding staircase fatigue data via the Murakami √Area model.It is shown that the fatigue method more effectively finds large inclusions than the other methods. It is also shown that the correlation between predictions of inclusion sizes by the √Area model from stress levels and fatigue initiating inclusions is weak forthis material.

sted, utgiver, år, opplag, sider
TMS (The Minerals, Metals & Materials Society) , 2007. s. 45-50
Emneord [en]
non-metallic inclusion, steel, gigacycle fatigue
HSV kategori
Forskningsprogram
Materialteknik
Identifikatorer
URN: urn:nbn:se:kau:diva-3516ISBN: 978-0-87339-704-9 (tryckt)OAI: oai:DiVA.org:kau-3516DiVA, id: diva2:158958
Konferanse
Fourth International Conference on Very High Cycle Fatigue (VHCF-4)
Tilgjengelig fra: 2009-02-05 Laget: 2009-02-05 Sist oppdatert: 2017-12-06bibliografisk kontrollert
Inngår i avhandling
1. Estimating inclusion content in high performance steels
Åpne denne publikasjonen i ny fane eller vindu >>Estimating inclusion content in high performance steels
2008 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Non-metallic inclusions in steel pose a major problem for the fatigue resistance, especially regarding fatigue at very long lives corresponding to low cyclic stress levels, as well as being detrimental to material toughness and polishability.

The largest inclusions are quite rare, which makes conventional detection methods timeconsuming if reliable results are to be obtained. Based on surface scanning using light or electron microscopes, these methods provide results that have to be converted to reflect the statistical volume distribution of inclusions.

Very high cycle fatigue (in the order of 109 cycles or more) using ultrasonic fatigue at 20 kHz has been found efficient at finding the largest inclusions in volumes of about 300 mm3 per specimen. The inclusions found at the fatigue initiation site can then been used to estimate the distribution of large inclusions using extreme value statistics.

In this work, a new method for estimating the volume distribution of large inclusions is presented as well as a suggested ranking variable based on the volume distribution.

Results from fatigue fractography and area scanning methods are compared to the endurance limit at 109 cycles for a number of batches from two high performance steels.

In addition, the extreme value distributions of fatigue initiating inclusions in six high performace steels, produced by different routes, are presented. It is shown that all modes of the Generalized Extreme Values distribution can be found in different materials. This result shows that the assumption of mode I distribution, also known as Gumbel or Largest Extreme Value distribution, must be substantiated.

sted, utgiver, år, opplag, sider
Karlstad: Karlstad University, 2008. s. 18
Serie
Karlstad University Studies, ISSN 1403-8099 ; 2008:50
HSV kategori
Forskningsprogram
Materialteknik
Identifikatorer
urn:nbn:se:kau:diva-3520 (URN)978-91-7063-207-5 (ISBN)
Presentation
2008-12-19, Ljungbergssalen, 21A 244, Karlstads universitet, Karlstad, 13:15 (svensk)
Opponent
Tilgjengelig fra: 2009-02-05 Laget: 2009-02-05 Sist oppdatert: 2011-11-29bibliografisk kontrollert
2. Large and rare: An extreme values approach to estimating the distribution of large defects in high-performance steels
Åpne denne publikasjonen i ny fane eller vindu >>Large and rare: An extreme values approach to estimating the distribution of large defects in high-performance steels
2011 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The presence of different types of defects is an important reality for manufacturers and users of engineering materials. Generally, the defects are either considered to be the unwanted products of impurities in the raw materials or to have been introduced during the manufacturing process. In high-quality steel materials, such as tool steel, the defects are usually non-metallic inclusions such as oxides or sulfides.

Traditional methods for purity control during standard manufacturing practice are usually based on the light optical microscopy scanning of polished surfaces and some statistical evaluation of the results. Yet, as the steel manufacturing process has improved, large defects have become increasingly rare. A major disadvantage of the traditional quality control methods is that the accuracy decreases proportionally to the increased rarity of the largest defects unless large areas are examined.

However, the use of very high cycle fatigue to 109 cycles has been shown to be a powerful method to locate the largest defects in steel samples. The distribution of the located defects may then be modelled using extreme value statistics.

This work presents new methods for determining the volume distribution of large defects in high-quality steels, based on ultrasonic fatigue and the Generalized Extreme Value (GEV) distribution. The methods have been developed and verified by extensive experimental testing, including over 400 fatigue test specimens. Further, a method for reducing the distributions into one single ranking variable has been proposed, as well as a way to estimate an ideal endurance strength at different life lengths using the observed defects and endurance limits. The methods can not only be used to discriminate between different materials made by different process routes, but also to differentiate between different batches of the same material.

It is also shown that all modes of the GEV are to be found in different steel materials, thereby challenging a common assumption that the Gumbel distribution, a special case of the GEV, is the appropriate distribution choice when determining the distribution of defects.

The new methods have been compared to traditional quality control methods used in common practice (surface scanning using LOM/SEM and ultrasound C-scan), and suggest a greater number of large defects present in the steel than could otherwise be detected.

sted, utgiver, år, opplag, sider
Karlstad: Karlstad University, 2011. s. 31
Serie
Karlstad University Studies, ISSN 1403-8099 ; 2011:47
Emneord
Non-metallic inclusions, Tool steel, Extreme value statistics, Distribution of defects, Generalized extreme values
HSV kategori
Forskningsprogram
Materialteknik
Identifikatorer
urn:nbn:se:kau:diva-8226 (URN)978-91-7063-382-9 (ISBN)
Disputas
2011-10-27, Eva Eriksson, 21A 342, Karlstads universitet, Karlstad, 13:15 (svensk)
Opponent
Veileder
Tilgjengelig fra: 2011-10-05 Laget: 2011-09-12 Sist oppdatert: 2011-10-25bibliografisk kontrollert

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