ARX model for experimental vibration analysis of grinding process by flexible manipulator
Bibliographic record
Abstract
Using a flexible manipulator for grinding process in situ has become a cost effective engineering service in the recent years, especially for repair and refurbish of mechanical systems and components. In comparison with traditional rigid robot manipulators, the flexible manipulator has proved its superiorities in terms of accuracy and efficiency. However, because of compact and flexible structure, concerns arise regarding its dynamic behavior during machining process. This paper introduces a method using an ARX (autoregressive with exogenous excitation) model to experimentally analyze the vibration by using a flexible robot during grinding operation in different cases Single Input – Single Output (SISO), Multi Input – Multi Output (MIMO). Simultaneously, a dynamometer allows for triaxial input excitations measurement while 3 accelerometers mounted at the end effector represent the vibration outputs of the whole process. Thanks to the operational modal analysis, the dynamical properties of the robot can be identified directly in operation. The results show that the ARX model is efficient for analyzing the operational vibration in complex systems with multi degrees of freedom and multi directions. The determination of modal parameters and identified Frequency Response Functions (FRFs) enable to predict the dynamical behavior of the system and to simulate the vibration in real working conditions. Further study is promising on the inverse problem to estimate the excitation forces while these later are not available and not practically measured in industrial applications.
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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".