Dynamic Mechanical Behavior of Metal at the Tip of a Plane Strain Crack

Krafft, J. M.

doi:10.1007/978-3-642-87445-1_7

Cited by 3 publications

(2 citation statements)

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“…The model is based on the recognition that crack growth instability (fracture) is governed by the 'tensile instability' (or necking failure) of ligaments in the crack-tip process zone ahead of the crack tip (see Krafft [13]). These ligaments have been identified with the regions of material isolated by the growth of voids nucleated at nonmetallic inclusions in high strength steels [14]. Using the stress analysis of Hutchinson, Rice and Rosengren [15][16][17] and the Hart-Li model for creep [18][19][20], the steady-state creep crack growth rate (da/dt) sm is related to the steady-state creep rate in the tensile ligament within the process zone, and the crack growth rate is given by the following equation:…”

Section: Mechanistically Based Modelling (Illustrative Examples)mentioning

confidence: 99%

“…The time-based fatigue crack growth rate is simply f (da/dN), where f is the frequency of cyclic loading. Experimental data suggest that, in practice, transition from pitting to fatigue crack growth is determined by the second criterion in equation (14). From equations ( 11) and ( 14), the transition crack size, a tr , may be determined by solving the following equality:…”

Section: Mechanistically Based Probability Modelling and Damage Evolu...mentioning

confidence: 99%

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Mechanistically based probability modelling, life prediction and reliability assessment

Wei

Harlow

2004

Modelling Simul. Mater. Sci. Eng.

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Life prediction and reliability assessment are essential components for the life-cycle engineering and management (LCEM) of modern engineered systems. These systems can range from microelectronic and bio-medical devices to large machinery and structures. To be effective, the underlying approach to LCEM must be transformed to embody mechanistically based probability modelling, vis-à-vis the more traditional experientially based statistical modelling, for predicting damage evolution and distribution. In this paper, the probability and statistical approaches are compared and differentiated. The process of model development on the basis of mechanistic understanding derived from critical experiments is illustrated through selected examples. The efficacy of this approach is illustrated through an example of the evolution and distribution of corrosion and corrosion fatigue damage in aluminium alloys in relation to aircraft that had been in long-term service.

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