Enhancing teaching and research skills in metal casting through a virtual casting lab
Bibliographic record
Abstract
Abstract This article describes the establishment of a virtual metal casting lab at King Fahd University of Petroleum and Minerals (KFUPM) and its relevance in engineering education (teaching and research). The main objective of this work is to communicate the importance of virtual metal casting in engineering education and research, and to provide a framework for establishing and developing a lab to expand research in the area. Casting simulation softwares are briefly introduced followed by the details of setting up the lab and utilization of MAGMASoft at KFUPM. This includes methods of acquiring licenses of the software and its training, collaboration with the local industries, learning and capacity building at KFUPM at several stages. The utilization of casting simulations in undergraduate and graduate teaching, and in funded research projects reflects the continuous development of teaching and research skills of students, instructors, and researchers at the institution. Evaluation of virtual casting lab is discussed. Invigorated by encouraging results of the existing lab, an extension in the lab is proposed leading to a virtual casting quality center at KFUPM. It is expected that the results presented in this study will encourage institutes, universities, and industries in the region to develop in‐house virtual casting facilities for expanding research and development in the field.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".