Characterization and experimental evaluation of gear transmission errors in an industrial robot
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper proposes a simple technique for assessing the effect of gear transmission errors in a six‐axis industrial serial robot, as these errors can vitally affect the industrial robot's positioning accuracy. Design/methodology/approach The experimental procedure is developed using a laser interferometer system to measure bidirectional linear position errors for an ABB IRB 1600 industrial robot. A simple technique based on fast Fourier transformation (FFT) analysis is devised and implemented for the characterization, evaluation, and quantification of gear transmission errors. Structural deformation and backlash error are also discussed. Findings The authors found that the major sources of error affecting the performance of the robot come from joints two and three. They also found that eccentricity errors, structural deformations, and backlash are the most important sources of error affecting the accuracy and the repeatability of the industrial robot studied. Additional tests show that the robot's first joint has relatively poor bidirectional repeatability. Practical implications The usefulness of a laser tracker (or any other large range portable 3D measurement system) is questionable for assessing – let alone analyzing in depth – the gear transmission errors of some of today's industrial robots. The authors demonstrate in this paper that a laser interferometer system can successfully measure gear transmission errors very accurately. The proposed methodology is simple, efficient, and easy to use for the characterization and quantification of the errors. Originality/value This work is the first to detail the use of the laser interferometer system for the characterization of the gear transmission errors of an industrial robot. A methodology has been developed and implemented for very accurately quantifying the effects of gear transmission errors, structural deformations, and backlash. The proposed methodology greatly simplifies the measurement set‐up and accelerates error quantification.
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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 | 0.002 | 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