Analisis Kegagalan Material Pada Sayap Pesawat Terbang (Review)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui mekanisme analisis kegagalan material yang dilakukan pada sayap pesawat terbang. Analisis dilakukan dalam bentuk inspeksi visual, fraktografi makro, dan fraktografi SEM. Analisis tersebut menggunakan uji komposisi dengan spektroskopi X-ray fluorescence (XRF) dan Oxford Instruments X-MET 5100, analisis makroskopik dengan kamera digital Nikon SMZ 1500 dan mikroskop stereo, dan analisis fraktografi pembesaran yang lebih tinggi dilakukan dengan menggunakan FEI XL40 SFEG SEM. Berdasarkan analisis yang dilakukan, ditemukan bahwa kegagalan terjadi pada komponen sayap pesawat, retakan intergranular karena kelemahan struktur mikro, adalah penyebab kegagalan material. Analisis kegagalan material sangat penting untuk mengetahui dan mengungkapkan penyebab dan penanggulangan komponen yang gagal. Bentuk kegagalannya bisa berupa retakan, patah tulang, korosi, dan lain-lain . This study aims to determine the mechanism of material failure analysis carried out on aircraft wings. The analysis was carried out in the form of visual inspection, macro-fractography, and SEM fractography. The analysis used composition assays with X-ray fluorescence (XRF) spectroscopy and Oxford Instruments X-MET 5100, macroscopic analysis with a Nikon SMZ 1500 digital camera and stereo microscope, and higher magnification fractographic analysis was performed using the FEI XL40 SFEG SEM. Based on the analysis carried out, it was found that failure occurred in the components of the aircraft wing, intergranular cracks due to microstructure weakness, were the cause of material failure. Material failure analysis is very important to know and reveal the causes and countermeasures of the components that failure. The form of failure can be in the form of cracks, fractures, corrosion, and others .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".