Research on a Machine Measurement Calibration Method Based on Laser Displacement Measurement
Bibliographic record
Abstract
Large irregular thin-walled components such as aviation structural components and aerospace structural components have large size specifications, complex structures, multiple machining features, and high accuracy requirements. During the machining process, the workpiece is prone to deformation, and product inspection and quality control are extremely important. In the operation process of industrial robot processing systems, frequent replacement of end tools and various vibrations and collisions during processing can cause tool positions to shift. Therefore, before operating the processing system, it is necessary to implement reasonable calibration of the true position of the end tools of the robot to ensure that the processing system has the necessary positioning accuracy. The use of traditional methods for error calibration of sensors without considering the measurement and processing of sensor displacement information leads to large errors in nonlinear error calibration results and poor calibration results. In response to this issue, this article proposes a method of using laser displacement sensors to autonomously calibrate the robot tool coordinate system. The principle is simple, reliable, highly automated, and easy to implement. This method achieves synchronous movement of the laser displacement sensor and the digital height gauge by developing a synchronous measuring fixture device, and eliminates installation errors by using a two axis fine adjustment device.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".