Smoothing and Reconstruction Strategy for Part Repair Using a Laser Displacement Sensor
Bibliographic record
Abstract
The reconstruction of the profile curve of damaged parts in the absence of an original technical drawing is an important process of part repair. Part damages are complicated and changeable. To reduce the material removal rate during repair and maintain a smooth and flawless profile curve after being repaired, a smoothing and reconstruction strategy using a laser displacement sensor to measure the profile curve was proposed in this study. This strategy established an adaptive measurement mechanism by building an automatic measurement motion platform. A staged repair and smoothing strategy of the profile curve measurement data was constructed on the basis of the locally weighted scatterplot smooth. A calculation model of a part repair processing curve was built by using the Akima interpolation. Moreover, a case study was conducted to verify the reconstruction accuracy of the strategy and smoothness of the reconstruction curve. Results demonstrate that the automatic measurement motion platform and adaptive measurement mechanism can realize an adaptive measurement of different profile structural shapes. The staged repair and smoothing strategy can repair the measurement error of profile curve measurement data, profile defects, and other abnormal data while maintaining the smoothness of the profile curve. After repair, the variation range in the measurement data error at different points of the profile curve decrease from (-1.4913, +0.0351) to (-0.3511, +0.3715). Akima interpolation can not only increase the maximum error of the processing curve but also decrease the material removal rate effectively and protect the smoothness of the profile curve. This study provides important guidance to increase the part repair efficiency and accuracy and realize the automatization of part repair.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".