Optimization of Additive Manufacturing Processes for Vehicle License Plates via Discrete Event Simulation and Cost Analysis
Bibliographic record
Abstract
The general purpose of this research was to develop a proposal for manufacturing vehicle license plates in Tegucigalpa, Francisco Morazán, using recycled PETG plastic filament in 3D printing through the use of industrial systems simulation.Part of the objectives was to identify critical stages of the process, for which the initial design of the plates was developed in SolidWorks and subsequently evaluated and adjusted using 3D printers like the Bambu Lab A1, identifying that the appropriate thickness should be at least 4 mm to ensure structural quality and proper layer adhesion and that the average printing time of part A would be 3 hours and 40 minutes, revealing that this factor would be the biggest obstacle to implementing this proposal.Through cost engineering, the economic benefits of manufacturing with recycled PETG were analyzed, demonstrating that this methodology reduces costs while promoting a sustainable and efficient additive manufacturing model.The analysis revealed that the proposed method yields a cost of L.90.53 per plate (this price is based on material cost), this cost being below the current cost of traditional plates which starting price is L.500 for two plates (this price includes all operational costs).The identification of critical stages in the process, supported by simulations, provided valuable insights for future large-scale implementations, offering an economically and environmentally responsible solution to the shortage of vehicle license plates in the country
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".