Modelling flow and work hardening behaviour of cold worked Zr–2.5Nb pressure tube material in the temperature range of 30–600̊C

Dureja, A.K.; Sinha, Sucharita; Pawaskar, Dnyanesh N.; Seshu, P.; Chakravartty, J.K.; Sinha, R.K.

doi:10.1016/j.nucengdes.2013.08.006

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…see (Luczynski et al, 2015), or for non-conventional conditions, e.g. see (Nguyen et al, 2013, Noh et al, 2014, Dureja et al, 2014, may not be readily available. Therefore, several trials must be conducted to identify the proper material constitutive model.…”

Section: Inverse Forward Stress-strain Identification Methodsmentioning

confidence: 99%

Flow stress identification of tubular materials using the progressive inverse identification method

Asaadi

Heyns

2016

Engineering Computations

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Purpose-Propose a progressive inverse identification algorithm to characterize flow stress of tubular materials from the material response, independent of choosing an a priori hardening constitutive model. Design/methodology/approach-In contrast to the conventional forward flow stress identification methods, the flow stress is characterized by a multi-linear curve rather than a limited number of hardening model parameters. The proposed algorithm optimizes the slopes and lengths of the curve increments simultaneously. The objective of the optimization is that the finite element simulation response of the test estimates the material response within a predefined accuracy. Findings-We employ the algorithm to identify flow stress of a 304 stainless steel tube in a tube bulge test as an example to illustrate application of the algorithm. Comparing response of the finite element simulation using the obtained flow stress with the material response shows that the method can accurately determine the flow stress of the tube. Practical implications-The obtained flow stress can be employed for more accurate finite element simulation of the metal forming processes as the material behaviour can be characterized in a similar state of stress as the target metal forming process. Moreover, since there is no need for a priori choosing the hardening model, there is no risk for choosing an improper hardening model, which in turn facilitates solving the inverse problem. Originality/value-The proposed algorithm is more efficient than the conventional inverse flow stress identification methods. In the latter, each attempt to select a more accurate hardening model, if it is available, result in constructing an entirely new inverse problem. However, this problem is avoided in the proposed algorithm.

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