Bibliographic record
Abstract
High precision positioning has become one of the most important features of a precision machine. Such a machine is required to provide versatility, speed and workspace and high precision positioning. Combining coarse (large stroke) and fine (high resolution) drive elements, connected in series, in a multi-element feed drive system provides the capacity of a large workspace with the property of high resolution motion. The performance of the whole system may be improved by adopting the merits of both drive elements to work in a complementary fashion. The multi-element feed drive concept has several applications in manufacturing, robotics and data storage. Fast tool servo in manufacturing is a direct use of the concept and its applications range from the creation of asymmetric surfaces to online chatter suppression. Micro-macro robots are also examples of multi-element feed drive systems that provide advantages when both large work space and accurate end-effector positioning are required. This paper presents an innovative design of a multi-element feed drive system for machine tools. It studies the design methodology and the implementation of the system and investigates several considerations that govern the design process and determine the performance. A multi-element feed drive setup based on a combination of PA and LM was built for experimental testing. Results show that the multi-element feed drive is able to improve the tracking performance as well as the steady state error. It also achieves faster settling time.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".