Deconvoluting cracking mechanisms in fusion processing of steel-copper multi-materials
Bibliographic record
Abstract
This study investigates various cracking mechanisms and their prevalence in fusion processing of steel-copper multi-materials using operando X-ray diffraction and imaging during laser powder-bed fusion (LPBF) of 316L-CuCrZr multi-material. Operando X-ray imaging helped identify three main types of cracking: (i) solidification cracking, (ii) metal-induced embrittlement (MIE), and (iii) liquation cracking. All cracking types are closely related to the phase formation during processing, leading to two underlying mechanisms. First, liquid-liquid phase separation (LLPS) and the monotectic reaction in the 316L-CuCrZr system lead to the formation of Cu-rich and Fe-rich liquids with vastly different solidification ranges, causing solidification cracking at the melt pool centers. Second, LLPS and the monotectic reaction distribute the Cu-rich liquid uniformly between the Fe-rich dendrites, leading to MIE and/or liquation cracking. X-ray computed tomography indicated that smaller but more frequent cracks form due to MIE/liquation cracking compared to solidification cracking. Further experiments showed that by avoiding phase separation via process adjustments, cracking can be drastically reduced. However, the complete elimination of cracking necessitates chemical alterations of the material feedstock, as observed in the crack-free examples in the literature. These findings serve as a guideline for understanding the underlying reasons of cracking in steel-copper multi-materials and optimizing processing to effectively mitigate cracking, while also quantifying the extent to which these adjustments can achieve this outcome.
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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 | 0.000 |
| 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 | 0.001 |
| 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".