Evolution of Electron Channelling Contrast Imaging of Plastic Deformation Induced by Berkovich Nanoindentation in Ferrite Steel
Bibliographic record
Abstract
Strain field characterization is critical for understanding plastic deformation in electrical and structural materials [1]. Electron Channelling Contrast Imaging (ECCI) and Electron Backscattered Diffraction (EBSD) methods can be used in a Scanning Electron Microscope (SEM) to study near-surface strain fields with nanoscale spatial resolution, a broad field of view, and statistically accurate data on a bulk sample [2]. In this work, we used the novel approach correlative ECCI to evaluate the microstructures and deformation evolution in a ferrite 1010 steel sample after nanoindentation. This technique uses the Bloch wave theorem to study the contrast obtained from lattice crystal defects in samples, including dislocations, twins, grain boundaries, and stacking faults [1]. ECCI measurements were made in the SEM SU 8000 from Hitachi with an accelerating voltage of 5kV. A solid-state Backscatter Electron (PD-BSE) detector was used to capture the contrast caused by crystal defects through electron channelling techniques [3], with EBSD measurements done with the SEM SU8230 from Hitachi at acceleration voltage of 15 kV. Geometrically Necessary Dislocations (GNDs) were identified using a cross-correlation-based EBSD with high angular resolution [4] and a Burgers vector of 2.48 nm. The average GNDs density was then calculated with the ATEX software. To extract the mechanical properties of the ferrite sample, experimental nanoindentation were carried out with the Hysitron system, while simulations were carried out using Finite Element Method in ABAQUS software, both with a load of 7500 µN. Figure 1a shows the average surface roughness of 13.76 ± 0.31 μm measured by zygo-profilometer. This determines the surface topology of the ferrite sample, to ensure a flat, smooth, and perpendicular surface to the nano indenter. The formation of equiaxed grains were observe in the microstructure of the 1010 ferrite sample, as shown in the ECCI image fig. 1b. The area of the nanoindentation 6 x 6 matrix is shown in the ECCI image in fig. 1c. Each indent shows up as an equilateral triangle located either inside a grain, at a grain boundary, or near a triple junction. It was observed that the channelling contrast varied across grains and around each indent. The variation in BSE intensity surrounding each indent was caused by local strain fields and the presence of deformation after indentation, as shown in fig. 1d. Figure 2a shows the histogram plot that was obtained through the EBSD orientation map on the sample, which gives an average of GND density of 8.13 x 1014 m-2. The load-displacement curve in figure 2b depicts the maximum indentation depth of 0.42 µm, reached at 7500 µN. An average hardness 2.39 ± 0.34 GPa was obtained, while 205.60 ± 9.72, and 207.00 GPa were obtained as the Young’s modulus for the experimental and computational analyses, respectively. Based on the strain field size measured from the ECCI image, the yield strength is aimed to be calculated in the future. Furthermore, materials from the indented surface would be removed using the ion milling surface preparation process. This would allow the evaluation of the microstructures and to know the magnitude of the plastic deformation induced at the maximum depth of the indents. (a) Surface topography obtained by zygo-profilometer, (b) ECCI image of the microstructure before nanoindentation, (c) ECCI image of the microstructure after nanoindentation, and (d) ECCI image on one of the indents. (a) Histogram showing GND distributions obtained from the EBSD map, (b) Experimental and simulated load-displacement curve for the ferrite steel.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".