Properties of Metal Extrusion Additive Manufacturing and Its Application in Digital Supply Chain Management
Bibliographic record
Abstract
The Fourth Industrial Revolution with a global demand for digital product life-cycle and the digital economy. The pervasiveness of internet of things, sensors, artificial intelligence, machine learning, and particularly digital manufacturing and digital supply chain management led to the formation and development of the digital economy. A digital device alters from an auxiliary tool to an essential production element when the cost of production is low and the needs for inventory is minimized, so that it can be used for different financial levels. In fact, the development of digital production systems and supply chain will not only affect how the supply chain is managed, but also will define the concurrency of the production with the other activities in product life-cycle. In this paper, the role of Material Extrusion Additive Manufacturing (MEAM) on Digital Production Systems and supply chain management is investigated. It is discussed how MEAM is comparable to the other metal 3D printing methods, in terms of machine cost, printing and post-processing. In addition, it is discussed that considering overall mechanical properties of the final products, production requirement, and cost what can be expected as the role of MEAM in digital production’s economy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".