Application of fuzzy logic (FL) method for crack detection in carbon/epoxy beams using buckling responses
Bibliographic record
Abstract
The present study follows the identification of crack orientations in industry-driven carbon/epoxy laminated composite beams (LCB) using buckling data through fuzzy logic application. The study proposed a novel buckling-based crack detection with fuzzy logic aid towards the assessment of health and functionality of LCBs. The critical buckling loads are numerically computed on ABAQUS finite element (FE) simulation software. Towards crack detection, an efficient hybrid Mamdani fuzzy inference system (FIS) is developed in the MATLAB platform. The hybrid FIS is formed by fusing three standalone membership functions (Gaussian, Trapezoidal and Triangular). The numerically computed first four modes of buckling loads are provided as input values to the hybrid Mamdani FIS and after defuzzification, the output is read as crack orientations. The output results are validated with the critical buckling loads experimentally arrived through INSTRON 8862 Universal Testing Machine (UTM). Furthermore, the present developed FIS is compared with other standalone methods (triangular, trapezoidal and Gaussian) and the analysis results highlight the superiority of the developed FIS in crack detection as it bears the closest resemblance to the experimental results. Besides, the approach reveals the process of feature learning and the crack detection in the buckling domain.
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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 | 0.001 | 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) | 0.002 | 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".