Implementation of 6R strategy in FDM printing process: Case: Small electronic enclosure box
Bibliographic record
Abstract
This paper provides an algorithm for the application of additive manufacturing in the sustainable development of the enterprise. The manufacturing process includes the process of manufacturing preparation, the process of realization of manufacturing (or remanufacturing), the end of the manufacturing process with additional processing and recycling process. A 6R strategy in the realization of new or redesign of existing elements/parts has been implemented. Additive manufacturing or FDM printing technology enables frequent and simple modification of the model at a customer's request, and prior to the model enters the manufacturing itself. The starting material for making the model was polylactic acid (PLA). This paper aims to present the procedure of the realization of an electronics enclosure for a miniature pressure transmitter on a 3D printer. This gives the designer the opportunity to correct existing errors, modify the product according to the requirements of end-users, or to design a completely new product (prototype). In order for the algorithm to get the right confirmation, it is important to design a product that enables: accessibility, easy interchangeability, disassembly, the possibility of finishing and reuse.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".