Data-driven analysis of acoustic emission signals for the distinction of deformation mechanisms
Bibliographic record
Abstract
The mechanical behavior of most materials involves discrete ``avalanche'' events, where a local microscopic volume within the bulk undergoes large abrupt deformations. Such events are commonly detected and analyzed based on the acoustic emission (AE) they generate. Many materials display several deformation mechanisms simultaneously, thus calling for analysis tools that can differentiate between the AE signals they produce. We introduce a new classification method where the power-spectral densities of AE signals are mapped onto a two-dimensional space by the $t$-distributed stochastic neighbor embedding (tSNE) algorithm. To study the effectiveness and accuracy of these tools, we produced ground-truth datasets of AE signals generated by dislocation plasticity, deformation twinning, and martensitic transformation. This is achieved by loading three different alloys, each characterized by a single deformation mechanism. We show that each dataset has different distributions of AE features, thus strengthening the necessity to distinguish between AE signals generated by different deformation mechanisms. Moreover, an analysis that does not separate deformation mechanisms, and instead considers the combined datasets, provides distributions that lead to specious conclusions. The two-dimensional representation applied by our data-driven method displays three distinct clusters, which are also identified by the $k$-means clustering algorithm. The clusters match the ground-truth datasets with an accuracy of 98.6%, implying the method's capability to distinguish between different deformation mechanisms that coexist in the same material.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".