Reliable adhesion failure qualification of metal-adhesive-metal assemblies using pulsed thermography
Bibliographic record
Abstract
The growth of application using multi-material assemblies in the transportation industry has resulted in structural adhesive becoming increasingly used. These assemblies, in addition to be subjected to mechanical stresses, are also exposed to harsh environments throughout the life of vehicles with temperature variations, high humidity and exposure to de-icing salts and fluids. While such assemblies are tested for mechanical strength and fatigue resistance, it is also critical to identify the failure mode of adhesive bonds to ensure that proper actions are taken to prevent catastrophic failure. Despite the obvious need to qualify the adhesive failure modes, this task is typically relegated to a semi-quantitative analysis of cohesive/adhesive failure ratios based on the visual inspection of an experienced eye, which along with the use of adhesives of various colors, inevitably introduces variability in the qualifying process. Moreover, the characterization of adhesive performance typically involves analysing hundreds of coupons and while the general failure types are known: bulk of the adhesive (cohesive failure), substrate/adhesive interface (adhesive failure) and near-interface; quantification on each coupon suffers from inaccuracy and better means are needed. In this work, we introduce the use of pulsed thermography (PT) as a repeatable and objective solution to quantify failure modes of metal-adhesive-metal assemblies by harnessing the fundamental differences in thermal properties of the two materials. It is shown that the inspection performed in through transmission mode allows for the distinction of the various adhesion failures.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".