Dimensional Adjustment for Assemblability of Rapid Protoyped Parts
Bibliographic record
Abstract
Recent advances in rapid prototyping technology make it a useful tool in assessing the early designs of not only individual parts but also assemblies. These rapid assemblies should allow the designers to evaluate the desired functional requirements for the actual fabricated parts. However, the rapid prototyping errors, especially shrinkage, make it difficult to emulate such functional requirements in the prototype. This paper presents an algorithm for the optimal adjustment of the nominal dimensions of rapid prototyped parts to maximize the probability of adherence to the assembly functional requirements. The proposed modification of the nominal dimensions compensates for shrinkage. In addition, the algorithm preserves the general shape of the parts. Real coded genetic algorithms are used to maximize the probability of adhering to those requirements and a truncated Monte Carlo simulation is used to evaluate it. Several examples have been used to demonstrate the developed algorithm and procedures. Guidelines have been presented for the applicability of this adjustment method for various types of fits. The proposed method allows the designers to experience more realistically the intended fit and feel of actual manufactured parts assemblies.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".