Dynamic model and modal testing for vibration analysis of robotic grinding process with a 6DOF flexible-joint manipulator
Bibliographic record
Abstract
Robot manipulators play an important role in industrial automation. Various aspects of robotic systems were subject of intensive investigations in the past, but the vibration problems in robotic machining processes have been rarely treated in the available literature. In this paper we present dynamic modeling of an ongoing research to study chatter vibration in robotic grinding process using a portable manipulator for rectifying the surfaces of hydro-electric equipments. This special-purpose robot manipulator was developed to automate on-site repairs such as grinding of eroded surfaces, depositing overlay welding and hammer peening. In this study, the structure of robot as the tool holder mechanism of the machining operation is modeled by articulated rigid bodies with flexible joints. The dynamic equations of the 6DOF flexible-joint manipulator are established using Lagrangian formulation. Impulsive grinding forces and periodically perturbed excitations existing in the process are exerted on the model to simulate its response. As an intuitive estimation for joints' stiffness parameters of the model, payload test experiments were performed on the robot. To validate predictions of the dynamic model regarding vibratory behavior, modal testing experiments were performed and measured natural frequencies and mode shapes were compared to their analytical equivalents. Some future trends of the research work are also addressed.
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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 | 0.000 | 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) | 0.001 | 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 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".