Damage Mechanism Characterization of Glass Fiber-Reinforced Polymer Composites: A Study Using Acoustic Emission Technique and Unsupervised Machine Learning Algorithms
Bibliographic record
Abstract
Recent advancements in composite materials design have made glass fiber-reinforced polymer composites (GFRPC) a viable choice for a wide range of engineering and industrial applications. Although GFRPCs boast attractive characteristics such as low specific mass and high specific mechanical strength, identifying and characterizing damage mechanisms in these materials is challenging. Several scientific studies have examined the root causes of GFRPC failure using various methods, including non-destructive techniques and learning algorithms. Despite this, ongoing investigations aim to accurately detect mechanical defects in GFRPCs. This study explores the use of non-destructive testing (NDT) combined with unsupervised learning algorithms to identify and classify damage mechanisms in GFRPCs. The NDT method employed in this study is acoustic emission (AE), which identifies waveforms associated with various failure mechanisms during testing. These waveforms are categorized using unsupervised learning methods such as principal component analysis (PCA) and self-organizing maps. PCA selects the most appropriate AE descriptors for distinguishing between different damage mechanisms, while the self-organizing maps algorithm performs clustering analysis and classifies failure mechanisms. Scanning electron microscope images of the observed failures are provided to sup-port the findings derived from AE data.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".