Modeling and assessment of the backlash error of an industrial robot
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY This paper proposes an experimental approach for evaluating the backlash error of an ABB IRB 1600 industrial serial robot under various conditions using a laser interferometer measurement instrument. The effects of the backlash error are assessed by experiments conducted on horizontal and vertical paths. A polynomial model was used to represent the relationship between the backlash error and the robot configuration. A strategy based on statistical tests was developed to choose the degree of polynomial representing the effect of the tool center point (TCP) speed and payload. Results show that the backlash error strongly affects the repeatability of the industrial robot. Statistical analyses prove that the backlash is highly dependent on both robot configuration and TCP speed, whereas it remains nearly unaffected by changes in the payload. It was discovered that the backlash error as measured at the TCP may exceeds 100 μm, and that the positive backlash error increases and the negative backlash error decreases when there is increase in TCP speed.
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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