X-ray diffraction-based estimation of remaining fatigue life in AA6061-T6 for “additive remanufacturing®”
Bibliographic record
Abstract
Cold spray is an advanced additive manufacturing technology that is capable of restoration of damaged metallic components without exposing them to high temperatures. To expand the use of cold spray from restoring the geometry and structure of defective parts to full remanufacturing, extending their lifespan beyond the original life cycle by replacing internally damaged areas (typically only 10–15% of the part’s volume), the first step is to accurately assess the damage at the part’s hot spot. This study explores the capabilities of X-ray diffraction (XRD) as a non-destructive testing method for assessing damage in AA6061-T6. A set of dog-bone samples was prepared to introduce controlled damage at different levels. X-ray diffraction measurements were conducted on these samples to generate test data, to assess dislocation densities. These values offer a quantified measure of internal damage and provide insight into the microstructural evolution under fatigue loading. By using this method, this study aims to develop a reliable method for pre-additive remanufacturing® damage assessment. In corroboration with earlier studies, we show that XRD can effectively detect internal material damage using dislocation densities through XRD-measured parameters such as full width at half maxima (FWHM), a measure of XRD peak broadening used for analyzing dislocation and strain. Integrating XRD-based damage assessment with cold spray additive manufacturing can enable precise and localized repairs. By implementing cold spray remanufacturing, this method can significantly reduce material waste, a major contributor to the greenhouse gas emissions, and extend components' lifespans across various industries, promoting sustainability and circular economy.
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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) | 0.000 | 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 | 0.001 | 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".