Investigating the Fusion Weldability of Al7075 and Solidification Crack Elimination using TiC Nanoparticles
Bibliographic record
Abstract
Due to one of the highest strength-to-weight ratios among aluminum alloys, Al7075 is a popular alloy in the automotive and aerospace industries.However, the widespread application of this precipitation-hardened alloy is significantly hindered by limitations in manufacturing processes, such as casting and fusion welding, mainly due to solidification cracks.Several investigations have been conducted to mitigate this issue through welding parameter optimization, adjusting the fusion zone composition, or adopting a solid-state welding process instead.Recently, several studies reported crack-free joints after fusion welding using nanoparticles, but the governing mechanisms are not yet clearly understood.Therefore, the first stage of this research investigates the micro-mechanisms behind solidification crack elimination in fusion welding of Al7075 alloy using 1 vol.%TiCnanoparticle enhanced Al7075 filler metal.By incorporating the TiC nanoparticles into the welding process, the study proposes two critical mechanisms in solidification crack elimination: (i) fusion zone grain morphology and size alternation due to the presence of TiC-nanoparticles in the early stage of solidification, acting as nucleation sites; and (ii) deleterious continuous eutectic precipitates replacement with favorable discontinuous ones.In the second stage of this research, a modified physical model is presented, illuminating the role of fusion grain morphology alternation in reducing solidification crack susceptibility.The modified proposed model showed that altering the fusion zone grain morphology from dendritic to equiaxed (i) reduces strain accumulation on the coherent solid network in the mushy zone by reducing the coherency temperature, and (ii) prevents This journey of academic and personal growth would not have been possible without the support and guidance of many.I extend my deepest gratitude to those who have been instrumental in this endeavor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".