A computationally efficient model to capture the inertia of the piezoelectric stack in impact drive mechanism in the case of the in-pipe inspection application
Bibliographic record
Abstract
Abstract. This paper presents a new model for the piezoelectric actuator (PA) in the context of in the impact drive mechanism (IDM) for the in-pipe inspection application. The feature of the model is capturing the inertia of PA stack in a distributed manner as opposed to the lumped manner in literature. The benefit arising from this feature is a balanced trade-off between computational efficiency and model accuracy. The study presented in this paper included both theoretical development (i.e. the model of the piezoelectric actuator and the model of the entire IDM which includes the actuator) and experimental verification of the model. The study has shown that (1) the inertia of the PA in such a robot will significantly affect the accuracy of the entire model of IDM and (2) the simulation of the dynamic behavior with the proposed model is sufficiently accurate by comparing with the experiment. It is thus recommended that the inertia of the PA be considered in the entire model of the IDM robot. The model is an analytical type, which has a high potential to be used for the model-based control of the IDM robot and optimization of its design for a much improved performance of the IDM system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".