The investigation of the influence factors of repairing quality during the laser directed energy deposition repair process
Bibliographic record
Abstract
Purpose The purpose of this study is to analyze the molten pool dynamic behaviors during the high quality repair process and the influence factors of repairing quality in the laser directed energy deposition (LDED) repair process. This study aims to improve the service performance of metal parts. Design/methodology/approach This paper develops a dynamic model for LDED repair process by considering the interaction of laser beam and powder stream. The repaired surface morphologies under the different process parameter conditions are calculated and the corresponding repairing quality is evaluated by the quantitative evaluation method. The influence factors on the repairing quality are analyzed by the Pearson correlation coefficient. Findings The quality of the surface repaired under the condition with appropriate powder feeding amount per unit length is higher than others. The molten pool dynamic behaviors characteristics with the stable temperature distribution and molten metal flow are beneficial for stable repair process and repaired surface formation with high quality. The depth of molten pool, heat dissipation and average mass-averaged mean kinetic energy have great influences on the repairing quality. Originality/value The proposed method can provide the excellent condition for the desired molten pool dynamic behaviors characteristics in the LDED repair process. The obtained results are efficient for improving the repairing quality and service performance of metal parts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".