Simulation of the nanoindentation response in single-crystal magnesium using crystal-plasticity finite element methods
Bibliographic record
Abstract
Nanoindentation is a widely used technique for characterizing the mechanical properties of materials at the nanoscale by applying controlled force through a sharp indenter and measuring the resulting material deformation. However, it is challenging to access the complex stress states during elastic-plastic deformations occurring beneath the indenter, which are crucial to bridge the microscopic plastic deformation carriers, dislocations and their slip systems, to the macroscopic mechanical behavior of the material. To address this, finite element simulation of nanoindentation is essential. Conventional mate-rial models in finite element software, such as ANSYS, are typically developed for bulk materials and are unable to accurately describe the microscopic plastic behaviors, such as the activation of slip systems in crystalline structures. Crystal plasticity finite element methods (CPFEM), which incorporate slip systems, are currently available only in the ABAQUS CAE finite element software. To further improve efficiency and flexibility of CPFEM, a custom user-defined material subroutine within the ANSYS Parametric Design Language (APDL) platform was developed in this thesis. This method accounts for both elastic and plastic crystal deformation, as well as the crystal plasticity constitutive laws specific to the deformation behaviour of the hexagonal close-packed structure of magnesium. In this subroutine, the elastic component of the material model was validated against experimental elastic modulus values from the literature, accounting for various crystallographic orientations. The plastic component was validated using Kelley-Hosford plane strain compression test data and compared with the simulation results of Graff et al. The material model was then integrated with the nanoindentation loading conditions, to determine the complex stress states in each element and the load-displacement curves for single-crystal magnesium along c-axis, a-axis, and six other grain orientations. Finally, the activity of basal, prismatic, and pyramidal slip systems was analyzed for each orientation. This work provides an open-source material subroutine designed to predict the crystallographic dependent mechanical behavior of magnesium. By clarifying the details of microscopic plastic deformation, this tool provides valuable insights for designing advanced magnesium for applications in the aerospace, automotive, and biomedical fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".