Defect Types
Bibliographic record
Abstract
Abstract This chapter provides an overview of the common types of defects found in various structural materials and joints in aircraft. Materials manufacturing methods (including large-scale production) have been established in the aircraft industry. However, as will be seen in this chapter, manufacturing defects and defects during in-service conditions are very common across all material types. The structural material types include metals, composites, coatings, adhesively bonded and stir-welded joints. This chapter describes the defect types as a baseline for the description of their detection with the methods of Chap. 10.1007/978-3-030-72192-3_5 to 10.1007/978-3-030-72192-3_8 . Based on the understanding of the defect types, there is great expectation for a technical breakthrough for the application of structural health monitoring (SHM) damage detection systems, where continuous monitoring and assessment with high throughput and yield will produce the desired structural integrity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.179 | 0.117 |
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".