A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy
Bibliographic record
Abstract
This work presents a set of comprehensive virtual experiments to detect material deformation and failure. The most commonly used pieces of equipment in mechanics and material disciplines, such as a metallographic cutting machine and a high-temperature universal creep testing machine, are integrated into a web-based system to provide different experimental services to users in an immersive and interactive learning environment. The protocol in this work is divided into five subsections, namely, the preparation of the materials, molding the specimen, specimen characterization, specimen loading, nanoindenter installation, and SEM in situ experiments, and this protocol aims to provide an opportunity for users regarding the recognition of different equipment and the corresponding operations, as well as the enhancement of laboratory awareness, etc., using a virtual simulation approach. To provide clear guidance for the experiment, the system highlights the equipment/specimen to be used in the next step and marks the pathway that leads to the equipment with a conspicuous arrow. To mimic the hands-on experiment as closely as possible, we designed and developed a three-dimensional laboratory room, equipment, operations, and experimental procedures. Moreover, the virtual system also considers interactive exercises and registration before using chemicals during the experiment. Incorrect operations are also allowed, resulting in a warning message informing the user. The system can provide interactive and visualized experiments to users at different levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".