| EPSRC Reference: |
GR/M39756/01 |
| Title: |
ESTIMATION OF THE MAXIMUM INCLUSION IN CLEAN STEELS AND THE RELATIONSHIP WITH MECHANICAL PROPERTIES |
| Principal Investigator: |
Atkinson, Professor HV |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Materials Science and Engineering |
| Organisation: |
University of Sheffield |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 February 1999 |
Ends: |
28 February 2002 |
Value (£): |
229,315
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| EPSRC Research Topic Classifications: |
| Materials Characterisation |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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| Related Grants: |
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| Panel History: |
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Summary on Grant Application Form |
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In clean steels there are many fine inclusions, and a few large ones which have a particularly harmful influence. However, they are usually difficult to detect, especially in large volumes. Observations are generally carried out on small volumes and the maximum inclusion size in say, a full cast, must be obtained by prediction based on statistical analysis. This prediction is therefore, a key issue for the makers and users of clean steels. The extrapolation can be made by fitting the observed inclusion size distribution with e.g. the log-normal but this has drawbacks. These can be avoided by using methods based on extreme value theory, either measuring sizes of inclusions larger than a chosen size (the Generalised Pareto Method (GPD) ), or measuring the maxima in randomly selected areas or volumes (the method of Murkami and co-workers). The proposers have recently been the first to apply the GPD method to steels and compare the results with those from the Murakami method. The crucial outcome is that the GPD method predicts an upper limit for the inclusion size whereas the Murakami method does not. The proposal aims to validate and further extend the GPD method which potentially has applications far beyond steels. In clean steels there are many fine inclusions, and a few large ones which have a particularly harmful influence. However, they are usually difficult to detect, especially in large volumes. Observations are generally carried out on small volumes and the maximum inclusion size in say, a full cast, must be obtained by prediction based on statistical analysis. This prediction is therefore, a key issue for the makers and users of clean steels. The extrapolation can be made by fitting the observed inclusion size distribution with e.g. the log-normal but this has drawbacks. These can be avoided by using methods based on extreme value theory, either measuring sizes of inclusions larger than a chosen size (the Generalised Pareto Method (GPD) ), or measuring the maxima in randomly selected areas or volumes (the method of Murkami and co-workers). The proposers have recently been the first to apply the GPD method to steels and compare the results with those from the Murakami method. The crucial outcome is that the GPD method predicts an upper limit for the inclusion size whereas the Murakami method does not. The proposal aims to validate and further extend the GPD method which potentially has applications far beyond steels.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.shef.ac.uk |