A Retrofit Open Architecture Parallel Drive CMM System
Bibliographic record
Abstract
Many previously installed Coordinate Measuring Machines (CMMs) are equipped with only a touch trigger probe. Data collection rates are slower than one point per second, and geometric error correction was applied only after the data point was recorded. Most commonly, the CMM structure was a bridge style, supported by air bearings above a granite table, and with the corresponding axis driven from only one side. It is assumed that the low speeds and accelerations do not introduce an uncorrected dynamic error. With the introduction of continues analog (scanning) touch probes and non-contact laser digitizers, CMM system improvements are required to support the new sensors, provide higher data point collection rates, and to manage dynamic error. This paper describes a retrofit open architecture system that provides these improvements. The control computer is based on an Intel Pentium processor, uses the Phar Lap ETS real-time operating system, and is implemented as an embedded system. Setup, including PID tuning, is accomplished remotely using ethernet and an external graphical user interface. Geometric error compensation is applied continuously along the entire motion trajectory. Uncorrected dynamic yaw error was significantly reduced by adding a parallel drive to the opposite side of the CMM bridge, together with a cross-coupling control algorithm that adjusts the reference position signals that are sent to the two parallel motion control loops. The effect is to speed up the lagging side, and slow down the leading side so as to minimize the differential error. Experimental results illustrate the improved dynamic performance of the system.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".