Bibliographic record
Abstract
In this thesis, a new approach is presented for the modelling and control of an automated polishing/deburring process that utilizes a dual-purpose complaint toolhead mounted on a parallel tripod robot. This toolhead has a pneumatic spindle that can be extended and retracted by three pneumatic actuators to provide tool compliance. By integrating a pressure sensor and a linear encoder, this toolhead can be used for polishing and deburring. For the polishing open-loop control, the desired tool pressure is pre-planned based on the given part geometry. To improve control performance, a closed-loop controller is applied for pressure tracking through pressure sensing. For the deburring control, another closed-loop controller is applied to regulate the tool length through tool extension sensing. The two control methods have been tested and implemented on a polishing/deburring robot, and the experiment results demonstrate the effectiveness of the presented methods. To future improve the control performance, an adaptive controller is developed to deal with the uncertainties in the compliant tool. This control method combines the adaptive control theory with the constant stress theory of the contact model. A recursive last squares (RLS) estimator is developed to estimate the pneumatic plant model, and then a minimum-degree pole placement (MDPP) is applied to design a self-tuning controller. Afterwards, the simulation and experiment results of the proposed controller are presented and discussed. Finally, a nonlinear model of the pneumatic plant is developed. The nonlinear controller developed by using feedback linearization method is applied on the nonlinear pneumatic system of the compliant toolhead. The simulation is carried out to test the effectiveness of the pressure tracking for the polishing process.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".