Frequency Comparison Function Method for Real-Time Identification of Breathing Crack at Welding Joint
Bibliographic record
Abstract
In this research, the frequency comparison function (FCF) method is proposed and studied to realize high-sensitive real-time crack identification at the welding joint area for a beam-type structure. This method is derived from the frequency response function (FRF). During FCF, we use the response signal collected from the designated point of the structure instead of the excitation. The standard deviation of the FCF amplitude curve is calculated to detect and evaluate the possible crack and its induced vibration perturbations afterward. Vibration responses are simulated in ANSYS by the use of the finite element analysis of a welded beam structure, and these signals are then analyzed with the FCF algorithm. It is concluded that FCF is an efficient method for breathing crack identification and can be easily applied under different excitation conditions, including harmonic and random ones. Meanwhile, FCF can be applied without any pre-processing algorithms such as filtering and smoothing. So, it can be used for real-time crack identification. By combining the FCF with the smart coating sensor composed of piezoelectric layers, the crack identification with high sensitivity is realized. The crack is detectable at its very early stage (starting from 3% of the beam thickness). Experimental studies under harmonic and random excitations are processed, and the results prove high sensitivity and feasibility of the proposed crack detection method.
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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".