Title:
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Influences of load history on the cleavage fracture of steels
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As part of a safety assessment of a component or structure, it is necessary
to define in a rigorous manner the limits for its safe use and operation. This
requirement leads to a need for accurate descriptions of the conditions for
failure.
In determining safe operating limits for failure by fracture, current methods
are often overly pessimistic, especially following load history or in the presence
of residual stress. Such conservatisms may lead to overdesign and excessive
weight or premature removal of infrastructure from service.
A study was conducted, described in this thesis, on the influences of previous
load cycles on brittle fracture, primarily in A533B ferritic steel. Potential
influences of remnant stresses on measured fracture toughness were explored
by extracting test coupons from large scale welded components. Finite element
simulations and experimental stress measurement were used to infer the
effect on measured toughness.
Re-analysis of previously published experimental data highlighted a range
of limitations and practical problems with a number of current fracture criteria.
To investigate the issues highlighted in greater depth, a program of
fracture testing was conducted covering a wide range of specimen constraint
levels and considering specimens with and without prior load history.
The resulting fracture data set was used to study the applicability of numerous
local approach methods, as well as crack tip fracture parameters, in
terms of their transferability between geometries and ability to predict the
effects of load history.
It was shown that the effect of prior loading on fracture behaviour can
be extremely significant. It was seen that the local approach, if properly
calibrated, is able to predict the influence of load and geometry on fracture to
an acceptable accuracy. It was also seen that consideration of fracture, even
under brittle conditions, as a stress and strain controlled process improved the
quality of the model predictions.
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