Identification of frequency response functions of a flexible robot as tool-holder for robotic grinding process
Bibliographic record
Abstract
The Frequency Response Functions (FRF) are identified for a robot during normal grinding operation using three different identification approaches, namely the spectral method, the Autoregressive with exogenous excitation (ARX) model and the State Space (SS) model. A comparison is made to determine the performance of each approach. The results show that both the ARX and SS models can be used for identification of the FRF but the ARX provides the best performance at the same model order. The spectral estimation method exhibits the worst capacity for the identification of operational frequency response function. It is found from the FRF identification that the acceleration on the feed direction of the robotic grinding process is most sensitive to the excitation force. Further developments are ongoing on the use of these models for evaluating the displacement at the end effector and for controlling the vibration of operational mechanical systems in operation such as grinding.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".