Bibliographic record
Abstract
Automotive and aerospace sectors have a pressing need for structural components that are lighter and stronger, aiming to improve energy efficiencies and reduce anthropogenic environment. Steel has already a wide variety of structural applications in the transportation industry due to its excellent properties. To further reduce CO2 emissions, lightweight magnesium (Mg) and aluminum (Al) alloys have increasingly been used in the vehicle fabrication due to their lower density, higher specific strength and stiffness, excellent size stability and process ability. The structural application of these alloys inevitably involves welding and joining of similar Mg-to-Mg and Al-to-Al, and dissimilar Mg-to-Al, Mg-to-steel and Al-to-steel. Resistance spot welding produces coarse grains, large defects and thick brittle intermetallic compounds (IMCs) in the weld metal. Alternative solid-state welding processes are being considered such as ultrasonic spot welding (USW), which produces coalescence through the simultaneous application of localized high-frequency vibratory energy and moderate clamping forces. In this study, USW was successfully carried out on similar Mg alloy and dissimilar Mg-to-Al, Mg-to-steel and Al-to-steel alloys. The overall objective of this work is to gain a better understanding of the dominant factors determining the joint performance, with particular emphasis on the microstructural evolution, crystallographic texture, micro-hardness, lap shear strength, fatigue resistance, fatigue life prediction model and fracture analysis of similar and dissimilar USWed joints. Overall, USWed Mg-to-Mg is stronger and more consistent in terms of weldability than the dissimilar USWed Mg-to-Al, Mg-to-steel and Al-to-steel. This was attributed to the large volume of thick brittle IMCs and significantly higher welds center hardness in dissimilar metals welding, which is the main cause of joint failure. The IMCs were confirmed by XRD, EDS and micro-hardness measurement tests.. Therefore, another objective of this study is to minimize the presence of brittle IMCs and engineer an acceptable intermetallic layer to produce sound joints between Mg-to-Al, Mg-to-steel and Al-to-steel. A third material (tin foil or zinc coating) was placed in-between the work pieces. With this procedure, the lap shear strength of the welded samples was increased. The detailed microstructural characterization and mechanical properties of welded joints with an interlayer are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".