Bibliographic record
Abstract
Thin coatings of relatively stiff material on relatively lightweight and compliant substrates, such as a metal coating deposited on a polymer, can produce high performance-to-weight ratio structures for engineering applications dominated by bending and torsion. Additive manufacturing techniques combined with electrodeposition enable manufacturing of structures with complex geometries and very thin coatings relative to the bulk thickness. Topology optimization is an attractive tool for designing such structures, as it does not require intuition about the optimal topology beforehand. While topology optimization methods have been developed for coated structures, their use of ersatz material methods makes them unsuited to very thin coatings, such as those produced by electrodeposition. A mixed-dimensional finite element model is developed in this thesis to model very thin coatings, involving the development of a transition finite element. This model is incorporated into a level set topology optimization algorithm, which requires the development of a heuristic-based discrete topology change step. Numerical studies are presented for MBB-beam and L-beam problems for a variety of initial geometries. The mixed-dimensional algorithm is shown to produce results similar to a reference solution for an MBB beam with a relatively thick coating, and results are also shown for more realistic material properties and dimensions. Converged solutions exhibit multimodality and unintuitive features resulting from the algorithm falling into local minima. The type of topology optimization algorithm developed in this thesis is a step forward in the optimization of thin-coated structures, but development of additional heuristics and post-processing methods is required for practical use.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".