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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".