Application of Entropy in Crack Identification at Welding Joint With a New Smart Coating Sensor
Bibliographic record
Abstract
An identification module is designed and studied to detect and evaluate the cracks at the welding joint area using a new smart coating sensor and entropy measurement. A new piezoelectric composite coating is applied at a welding joint to possibly charge the wireless data transmission module as an energy harvester. It also sends warning and dynamic signals for crack evaluation when the crack damage occurs. More specifically, entropy calculation is introduced to quantify the weak perturbations, which is caused by the material nonlinearity and crack breathing at the crack tip and hidden in the signal. In this paper, a finite element model (FEM) of a welded beam experiencing dynamic base motion is established as an example. The effects of material nonlinearity and crack breathing on structural dynamics response are simulated by creating nonlinear material property around the crack area and contact pair of crack walls, respectively. After obtaining the time domain vibration signal, crack severity is quantified using Sample Entropy. It is concluded that, even at very early stages of 5% of the beam thickness for the crack depth, the entropy variation is significant for a damaged beam compared with the healthy one.
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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) | 0.000 | 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".