Optimization and visualization of rapid prototyping process parameters.
Bibliographic record
Abstract
The optimal selection of rapid prototyping (RP) process parameters is a great concern to RP designers. When dealing with this problem, different build objectives have to be taken into consideration. Using virtual rapid prototyping (VRP) systems as a visualization tool to verify the optimally selected process parameters will assist designers in taking critical decisions regarding modeling of prototypes. This will lead to substantial improvements in part accuracy using minimal number of iterations, and no physical fabrication until confident enough to do so. The purpose of this thesis is to demonstrate that virtual validation of optimally selected process parameters can significantly reduce time and effort spent on traditional RP experimentation. To achieve the goal of this thesis, a multi-objective optimization technique is proposed and a model is generated taking into consideration different build objectives, which are surface roughness, support structure volume, build time and dimensional accuracy. The multi-objective method used is the weighted sum method, where a single utility function has been formulated, which combines all the objective functions together. The orders of magnitudes have been normalized, and finally weights have been assigned for each objective function in order to create the general formulation. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .E47. Source: Masters Abstracts International, Volume: 43-03, page: 0959. Adviser: Waguih ElMaraghy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".