Preliminary Studies for Precision Polishing of Micro Structured Mold by Using Three-Dimensional Low Frequency Vibration Utilizing Piezoelectric Actuator Incorporated with Mechanical Amplitude Magnified Mechanism
Bibliographic record
Abstract
Precision polishing of micro structured mold has been highly demanded due to the increasing demand for optics manufacturing such as solar optics and DVD pick-up system, and medical devices like μ-TAS [1-5]. These micro structured molds usually have complicated structure and need to be polished after grinding or cutting. In this paper, a three-axis low frequency vibration (3DLFV) polishing actuator is proposed. The actuator consists of 3 multilayers-stacked piezoelectric actuators (PZT) incorporated with mechanical amplitude magnified mechanism. The mechanical amplitude magnified mechanism utilizes mechanical structures which is also called mechanical transformer, which is capable to elongate the stroke of the piezoelectric actuator to almost 13 times to 225 m. By driving the PZT in sine wave with particular phase different, dual direction trajectory such as circle can be achieved, and is proved to be effective in precision mold polishing [8]. With the 3DLFV actuations, polishing tool with polyurethane is actuated to stir the diamond slurry to achieve polishing effects. In polishing experiments, nickel-plating metal used as work pieces are polished with diamond slurry and the polished depths are measured. As a result, three-axis low frequency vibration (3DLFV) is proposed and developed. Its capability in polishing precise mold is studied and confirmed to be efficient. In order to improve the work piece surface, a dwell time control method can be applied with the 3DLFV.
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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 | 0.001 |
| 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) | 0.001 | 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".