Analysis of Weld Joint Strength on Galvanized Material Using Rb-26 Electrode
Bibliographic record
Abstract
Practical work is a form of education and vocational implementation that is followed by students so that students can work directly in the business world as well as industry or fabrication. Practical work aims to prepare students to become productive human beings and can immediately work in accordance with their respective fields, students can also feel the atmosphere of production and can help deal with some problems- problems experienced by Engineering professionals inside the factory. Therefore, in welding, knowledge must accompany practice, in more detail it can be said that the design of building construction and machines with welded joints, must also be planned about welding methods. This method of inspection, welding material, and type of weld to be used, based on the function of the building parts or machines designed. Based on the definition of DIN (Deutch Industrie Normen) Galvanized Welded Broken Products is the best way to assemble or connect constructions and products made from iron. This welding method is specifically performed for galvanized materials. The iron welding process requires special preparation and skills. Based on the results of fieldwork practices that have been carried out in CV. Sumber Agung Widodo, The process of making panel tables is carried out into several stages, namely the hollow iron cutting, 45-degree galvanized angle cutting, iron plate cutting, elbow iron cutting, splicing by welding, frame painting, and mounting a series of panels to the finished panel table. The materials used are hollow iron, iron plate, elbow iron, iron paint, wheels. The tools used are ac current welding machines, grinders and elbow rulers. Stage
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".