Bibliographic record
Abstract
Abstract Excessive and unstable vibrations that are caused by the machining forces are among the most critical problems that limit the use of industrial robots instead of CNC machine tools. Reduction and control of robot’s vibrations during machining require accurate models of the robot’s vibration response to the dynamic forces exerted at the Tool Centre Point (TCP) where the cutting tool interacts with the workpiece material. The existing models of vibrations in robotic machining have been formed by assuming the linearity of the dynamic response at the TCP. In this paper, the accuracy of this assumption is investigated experimentally, and the results show that the dynamic response at the TCP is strongly nonlinear. An experimental procedure is presented to identify the nonlinearities by employing the first-order Frequency Response Functions (FRFs) measured using various input force excitations. Nonlinear Complex Mode Analysis is then used to extract the modal parameters of the system when its dynamics is linearized around a harmonic response with a constant amplitude. The extracted modal parameters strongly depend on the amplitude of the applied force and the resulting vibrations. This study highlights the need for considering the nonlinearities of the structural dynamics of industrial robots in modelling machining vibrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".