Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drilling
Bibliographic record
Abstract
Delamination is a prevalent issue in carbon fiber-reinforced plastic (CFRP) drilling, significantly compromising the mechanical properties of the material. Considering that delamination can impact the long-term durability of the final products, it is essential for operators to promptly identify it. This paper proposes a machined surface image generation model, called Sensor2Image, that employs time-series force sensor data as input and generates drilled-hole surface images as output. Sensor2Image first encodes the force sensor data into images using the Gramian angular field (GAF) method. Subsequently, it applies an image-to-image translation technique to generate the final machined surface images. The proposed model was trained and evaluated using experimental data gathered from drilling CFRP specimens under an industrial robot machining system. The results demonstrated the versatility of the proposed model for practical applications, regardless of the delamination factor. The proposed method offers significant advantages over existing methods through its intuitive visual representation approach. It facilitates the visual inspection of delamination while enabling surface quality analysis of the drilled hole and identification of defects or irregularities that may impact the mechanical properties of the material. The proposed approach can enhance the efficiency and reliability of industrial processes, particularly those involving complex delamination factors. It is a valuable tool for optimizing the CFRP drilling process and enhancing drilled-hole quality in a user-friendly manner.
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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 | 0.001 |
| 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".