A comparative evaluation of three industrial robots using three reference measuring techniques
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present a technique for assessing and comparing the static and dynamic performance of three different models of small six-axis industrial robots using a Renishaw XL80 laser interferometer system, a FARO ION laser tracker and a Renishaw QC20-W telescoping ballbar. Design/methodology/approach – Specific test methods are proposed in this work, and each robot has been measured in a similar area of its working envelope. The laser interferometer measurement instrument is used to assess the static positioning performance along three linear and orthogonal paths. The laser tracker is used to assess the contouring performance at different tool center point (TCP) speeds along a triangular tool path, whereas the telescoping ballbar is used to assess the dynamic positioning performance for circular paths at different TCP speeds and trajectory radii. Findings – It is found that the tested robots behave differently, and that the static accuracy of these non-calibrated robots varies between 0.5 and 2.3 mm. On the other hand, results show that these three robots can provide acceptable corner tracking at low TCP speeds. However, a significant overshoot at the corner is observed at high TCP speed for all the robots tested. It was also found that the smallest increment of Cartesian displacement (Cartesian resolution) that can be taken by the tested robots is approximately 50 μm. Practical implications – The technique used in this paper allows extremely accurate diagnosis of the robot performance, which makes it possible for the robot user to determine whether the robot is in good or bad condition. It can also help the decision-maker to select the most suitable industrial robot to achieve the desired task with minimum cost and specific application ability. Originality/value – This paper proposed a new method based on the performance verification approach for solving the robot selection problem for flexible manufacturing systems. Furthermore, despite their importance, bidirectional repeatability and Cartesian resolution are never specified by the manufacturers of industrial robots nor are they described in the ISO 9283:1998 guide, and they are rarely the object of performance assessments. In this work, specific tests are performed to check and quantify the bidirectional repeatability and the Cartesian resolution of each robot.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".