Bibliographic record
Abstract
The polymer laminated sheet metal (PLSM) is a layered material which involves a sheet metal substrate, a thin polymer film and an adhesive layer between the film and the substrate. The adhesion properties between the bonded materials are among the most important issues in PLSM forming operations. In this thesis, the main focus has been devoted to characterizing and improving the adhesion properties of the PLSM system for forming applications. Metallic surface roughness evolution and residual stress development in polymer adherends are two consequences of the plastic deformation of the PLSMs. In chapter 2, the effect of these factors on interfacial adhesion strength between metallic substrate and polymer adherend (polymer film with a thin uniform pressure-sensitive adhesive layer on one side) is investigated by devising a new experimental methodology. This methodology is based on two different protocols for preparation of peel sample, one involving pre-straining in uniaxial tension of the metallic substrate prior to lamination and the other involving post-lamination pre-straining of the PLSM. In chapter 3, the peel test results of two different types of PLSMs at different peel speeds are analyzed with two different approaches common in cohesive zone modeling in the literature, namely linear elastic stiffness approach and critical maximum stress approach. The modeling results revealed the significance of the peel speed in determining the interface strength between the adhesive and metallic substrate. In chapter 4, two mechanical treatment techniques of grinding and knurling are implemented to alter the metallic substrate surface roughness before lamination. Peel strength of these samples are investigated at different peel speeds and at different peel loading directions with respect to the grinding and knurling directions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.053 | 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".