Effective applications of computer-vision techniques in packaging design concept evaluation
Bibliographic record
Abstract
In packaging design concept verification, interpretations of data obtained from test vehicles are preferably supported by physics based analysis. This is vital when correlation between the thermal/mechanical test data and results from accelerated life testing is sought in order to predict the service life of a prototype. A proposed approach that integrates experimental and analytical procedures has been devised for such analyses. A feature of the approach is the wide variety of the measured data. Other than the test parameters, the approach collects temperature and time dependent deformation data at board, component and interconnect levels. Measurements in this work include surface warpage, in-plane displacements, rigid-body rotation and strains, assembling or residual stress/strain, failure loads and strength limits, etc. From the raw data, further quantities were deduced by applying the theories of beams/plates and material constitutive relations to further enrich the database. Inferred data include surface curvature, cross-sectional bending moment and shearing force, stress, strain rate and strain energy density, etc. The data variety allows better confirmation of the consistency among test results obtained with different techniques. It also facilitates applications of different theories for life prediction and failure mode and root-cause diagnosis. The comparison of different model predictions for same problem is aimed to provide a guide to the reliability model selection and application. This paper presents the experimental and analytical procedures together with some application examples to illustrate the approach.
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 | 0.001 | 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".