A simulation approach to track quality, cost and time during the manufacture of metal bonded assemblies
Bibliographic record
Abstract
An important aspect of many quality improvement initiatives launched by manufacturing firms consists of either (i) making the rework and interim part scrap an integral part of their manufacturing process, or (ii) select a process with a process capability higher than cost Justifiable, or (iii) increasing the process automation. Whereas these efforts are justified, difficulty to quantify the benefits of the resulting "higher quality levels" has led many critics to stigmatize the initiative as a 'quality-at-any-cost ' scheme. In this thesis, a systematic problem solving approach is developed to (i) understand the process and the problem, (ii) identify the ources) of the problem, (iii) model the process, (iv) test the specification or the given conditions if they can be met, and (v) develop a simulation model for the process to allow continuous tracking of quality, cost and time. The prefit process used for verifying the required mating conditions for detail parts that make up the bonded assembly, and their specified dimensional tolerances were found to be the main problems causing assembly nonconformance. During the prefit process, the detail parts are made to fit like a jigsaw puzzle on a simulated bond tool, and gaps between all associated joints are verified to meet the requirements for the bond line thickness on the final bonded assembly. If the prefit gaps are not as per the requirements, the cured assembly will have either too thin or two thick bond lines, besides having disbonds, voids or porosity. If these defects are found at the final assembly, the parts have to be scrapped, as no rework is possible at this stage. It is therefore, imperative that attention is paid during the prefit stage. In this research, a simple spreadsheet simulation approach was developed to test the detail part specification and verify if it was theoretically possible to obtain the required gaps at various joints. The approach was then extended to determine the extent of gap compliance, and identify the conditions under which gap compliance could be achieved. Finally, a stochastic dynamic (SD) simulation model for the prefit process of atypical metal bonded assembly, the Main Landing Gear Door, was developed. (Abstract shortened by UMI.)
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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 | 0.000 | 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".