An investigation on mechanical properties of 3D pen fused zones for additive manufactured parts
Bibliographic record
Abstract
Additive manufacturing has been one of the most used techniques in the recent years because of its capabilities to fabricate complex structures as required by customer and industrial need from a 3D computer-aided design model without the usage of any tooling, dies and heavy machinery makes it a step ahead in the present manufacturing techniques. In the current study the author’s focus on the welding or joining of additive manufactured Polylactic acid (PLA) parts made by Fused Deposition Modeling (FDM). There are several techniques for welding these additive manufactured parts. This study mainly focuses on the joining of 3D printed PLA parts using a 3D pen and investigations on its mechanical properties experimentally. It is a very cheap and effective technique when compared to the other welding methods. This could overcome the drawback of small bed size in most 3D printers by joining smaller parts and it can also be used for repairing the defects caused during the 3D printing. Moreover the experimental testing of the mechanical properties also confirmed that the tensile, flexural and impact strength of 3D pen welded specimens retrieved above 70% of the strength to the original PLA specimen proving it to be a very effective method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".