Keyway Alignment Using GRNN in Robotic Pipe Handling
Why this work is in the frame
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Bibliographic record
Abstract
Perforation pipes are widely used in oil&gas exploration. Aligning a perforated pipe in different workstations is very important for quality control. Typically, a keyway in a perforated pipe is machined as the alignment feature. Once the angle of a keyway is measured, an industrial robot is used to rotate the perforated pipe to the desired location. However, due to various errors, the industrial robot cannot rotate the perforated pipe to the desired location using the measured angle once. In this paper, a method based on generalized regression neural network (GRNN) is proposed to predict the rotation angle such that an industrial robot can rotate a perforated pipe to the desired location. Training datasets are collected first to train a GRNN model. The model is then deployed to predict the rotation angle. Experiments were performed to validate the proposed method and the results show that the final keyway angle satisfies the requirement of keyway alignment.
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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 it