Virtual diffraction analysis of dislocations and dislocation networks in discrete dislocation dynamics simulations

Bamney, Darshan; Tallman, Aaron E.; Capolungo, Laurent; Spearot, Douglas E.

doi:10.1016/j.commatsci.2019.109473

Cited by 8 publications

(14 citation statements)

References 45 publications

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“…Last but not least, we believe that the dislocation density tensors may be the missing links for advancing the evaluation of X-ray diffraction patterns for other than just the GND information. Such a connection could be established from correlating the alignment tensors to virtual diffraction patterns [18][19][20] from DD results.…”

Section: Discussionmentioning

confidence: 99%

Analysing discrete dislocation data using alignment and curvature tensors

Weger¹,

Gupta²,

Hochrainer³

2021

Comptes Rendus. Physique

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Analysis of large scale discrete dislocation data requires the characterisation of complex dislocation networks by suitable average quantities. In the current work, we suggest dislocation alignment tensors and closely related curvature tensors as easily extractable and intelligible measures of geometrical and topological characteristics of dislocation distributions. We provide formulae for extracting these measures from discrete dislocation data based on straight segments. Examples for interpreting and visualising these measures are provided for a simple configuration and two more involved results from discrete dislocation simulations. We suggest the alignment and curvature tensors for wider use in plasticity research.

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Section: Discussionmentioning

confidence: 99%

Analysing discrete dislocation data using alignment and curvature tensors

Weger¹,

Gupta²,

Hochrainer³

2021

Comptes Rendus. Physique

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“…Second, for metals, the electric force may be so noticeable that it may dominate dislocation dynamics. Third, conclusions upon plastic deformations, as are built on classical dislocation dynamics where the electric force has been neglected [48], e.g., DDD [31][32][33][34][35][36][37][38][39][40][41][42], may need to be re-examined carefully by adding the electric force. A comparison between this theory and associated experimental observations needs to be performed in the future.…”

Section: Correction For Image Forcementioning

confidence: 99%

“…Through DDD simulations, the creep behaviors of Ni-based single-crystal superalloys at high temperatures were investigated [41]. In addition, according to DDD simulations, correlation between diffraction peak broadening and dislocation density in relaxed dislocation networks was studied [42]. For these DDD simulations, the interaction between dislocations was key and can yield an internal stress field that influences dislocation dynamics heavily.…”

Section: Introductionmentioning

confidence: 99%

Electric features of dislocations and electric force between dislocations

Huang

2020

Mathematics and Mechanics of Solids

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Dislocations and dislocation dynamics are the cores of material plasticity. In this work, the electric features of dislocations were investigated theoretically. An intrinsic electric field around a single dislocation was revealed. In addition to the well-known Peach–Koehler force, it was established that an important intrinsic electric force exists between dislocations, which is uncovered here for the first time and has been neglected since the discovery of dislocations. The electric forces may be large and sometimes could exceed the Peach–Koehler force for metals and some dielectric materials with large dielectric constant. Therefore, the electric force is anticipated to play a vital role in dislocation dynamics and material plasticity. Moreover, an external electric field could exert an electric force on dislocations and a threshold electric field was subsequently discovered above which this force enables dislocations to glide. Interestingly, it was found that some dislocations move in one direction, but others move in reverse in an identical electric field, which is in agreement with experimental observations. Despite dislocation motion under an electric field, to one’s surprise, both edge and screw dislocations do not carry net charges by themselves, which may tackle the long-standing puzzle on the charges of dislocations. These findings may supply people with new fundamental knowledge on dislocations as well as dislocation dynamics, and may assist people in understanding related phenomena.

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“…Nevertheless, both the modest number of cases considered (5) and the approximations made in generating the virtual profiles limited the general applicability of their approach. Since then, several continuum-based methods have been proposed to more accurately generate virtual profiles [51][52][53][54] .…”

mentioning

confidence: 99%

“…Recently, improvements to the speed and accuracy of virtual diffraction algorithms 6,[50][51][52][53][54][55] and to the efficiency of mesoscale simulations 56 have enabled the rapid generation of hundreds of diffraction profile-microstructure pairs 52 . For example, Bamney et al 52 developed two strainbased virtual diffraction algorithms, one using the Stokes-Wilson approximation 34 , the other using differential strains to include the effects of spatially correlated strains on broadening. In the same work, a data-driven DLPA model, on which the present work is based, was proposed 52 .…”

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confidence: 99%

Data-Driven Analysis of Neutron Diffraction Line Profiles: Application to Plastically Deformed Ta

et al. 2020

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Non-destructive evaluation of plastically deformed metals, particularly diffraction line profile analysis (DLPA), is valuable both to estimate dislocation densities and arrangements and to validate microstructure-aware constitutive models. To date, the interpretation of whole line diffraction profiles relies on the use of semi-analytical models such as the extended convolutional multiple whole profile (eCMWP) method. This study introduces and validates two data-driven DLPA models to extract dislocation densities from experimentally gathered whole line diffraction profiles. Using two distinct virtual diffraction models accounting for both strain and instrument induced broadening, a database of virtual diffraction whole line profiles of Ta single crystals is generated using discrete dislocation dynamics. The databases are mined to create Gaussian process regression-based surrogate models, allowing dislocation densities to be extracted from experimental profiles. The method is validated against 11 experimentally gathered whole line diffraction profiles from plastically deformed Ta polycrystals. The newly

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Virtual diffraction analysis of dislocations and dislocation networks in discrete dislocation dynamics simulations

Cited by 8 publications

References 45 publications

Analysing discrete dislocation data using alignment and curvature tensors

Analysing discrete dislocation data using alignment and curvature tensors

Electric features of dislocations and electric force between dislocations

Data-Driven Analysis of Neutron Diffraction Line Profiles: Application to Plastically Deformed Ta

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