Wide-Focus Imaging for Industrial Metal Workpieces Using Tower-Type Transmitting Coils
Bibliographic record
Abstract
Metal workpiece imaging is of great significance for industrial nondestructive testing. However, current methods, such as radiographic or electromagnetic induction, are easily influenced by background noise, which leads to a short detection range and a high false detection rate. To overcome this issue, this article proposes a wide-focus imaging method for industrial metal workpieces using a tower-type transmitting coil and a magnetic sensing array. First, a single-loop coil excitation model is established to analyze the effects of transmitting current, coil radius, and other parameters on the radiation magnetic field. Second, a coaxial noncoplanar tower-type coil is designed, and a wide-focus optimization is achieved using a particle swarm algorithm, reconciling the conflict between high uniformity and high focusing degree requirements. Third, a wide-focus imaging system is developed, which combines a magnetic sensing array with cubic convolution interpolation algorithms to achieve 3-D imaging of metal workpieces. Experimental results demonstrate that the magnetic field uniformity achieved by the tower-type transmitting coil reaches 98.02%, representing a 43.82% improvement over the conventional single-loop coil. The proposed wide-focus imaging method leads to an average enhancement of 16.18% in imaging contrast and an average improvement of 37.33% in detection distance under various conditions, which increases the environmental adaptivity and decreases the false detection rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".