Bibliographic record
Abstract
Semi-solid metal (SSM) processing, as a viable alternative manufacturing route to those of conventional casting and forging, has not yet been fully exploited despite nearly half a century since its introduction to the metal industry. The slow pace of adopting SSM routes may be due to various reasons, including capital costs, profit margins, and, most importantly, the lack of detailed analysis of various SSM processes in open literature to confidently establish their advantages over more conventional routes. Therefore, the SSM community must disseminate their findings more effectively to generate increased confidence in SSM processes in the eyes of our industrial leaders. As such, we have embarked on the task to invite the leaders in SSM research to share their findings in a Special Issue dedicated to semi-solid processing of metals and composites. SSM processing takes advantage of both forming and shaping characteristics usually employed for liquid and solid materials. In the absence of shear forces, the semi-solid metal has similar characteristics to solids, i.e., easily transferred and shaped; by applying a defined force, the viscosity is reduced and the material flows like a liquid. These unique dual characteristics have made SSM routes attractive alternatives to conventional casting on an industrial scale. With the intention of taking full advantage of SSM characteristics, it is crucial to understand SSM processing, including topics such as solidification and structural evolution, flow behavior through modelling and rheology, new processes and process control, alloy development, and properties in general. This Special Issue focuses on the recent research and findings in the field with the aim of filling the gap between industry and academia, and to shed light on some of the fundamentals of science and technology of semi-solid processing.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".