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Record W3122239909 · doi:10.5937/jemc2002141v

Implementation of 6R strategy in FDM printing process: Case: Small electronic enclosure box

2020· article· en· W3122239909 on OpenAlexaff
Miloš Vorkapić, Mohammad Al Hasan, Dragoljub Tanović, Marija Baltić, Branislav Tomić

Bibliographic record

VenueJournal of Engineering Management and Competitiveness · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsBombardier (Canada)
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsManufacturing engineeringInterchangeabilityProcess (computing)Remanufacturing3D printingRealization (probability)ReuseProduct (mathematics)Computer scienceProduct designElectronicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.222
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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