Design and Analysis of a Robotic End-Effector for Automated Hi-Lok Nut Installation
Bibliographic record
Abstract
The automated installation of hi-lok nuts by the robot is an effective way to replace tedious manual labor. For this purpose, an appropriate end-effector needs to be designed to carry out the feeding, alignment and fastening tasks. According to the installation process of hi-lok nuts, a motor driven fastening tool is designed with two parts: the front nut runner and rear driving shaft. The fastening task is modeled based on the force balances in the nut screwing action, which present the nut runner can rotate the nut as well as feed it axially. Then, a feeding-alignment (FA) device is designed to engage the nut feeding for fastening tool. The alignment action is modeled through the force balance about hi-lok nut involved with the nut gripper and nut runner. Finally, a tool end-effector has been built and integrated with an industrial robot. The successful implementation of automated installation of hi-lok nut demonstrates the effectiveness of the proposed installation method and the validation of the designed robotic end-effector.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".