ANALISIS FOTO MAKRO TERHADAP KEKASARAN BAJA ST 60 HASIL PEMBUBUTAN FACING DI MESIN CNC HARDINGE
Bibliographic record
Abstract
Technological advances have made the manufacturing sector an industry that needs to develop and compete globally, one of which is CNC machines which produce quality products for industrial needs. In producing a material at CNC machine, the latest design software is needed, namely Autodesk Fusion 360. Apart from lest, on then machine processing, it is desired to produce a material surface with a good roughness value. The desired result of this research is to analyze roughness value towards facing turning results on CNC machines for variations in spindle speed, namely 410 rpm, 450 rpm, 660 rpm, 900 rpm, 1100 rpm and 1200 rpm using carbide chisels and dromus coolant. The roughness of the facing surface can be assessed using a Surface Roughness Tester and analyzing the material structure using macro photos. The results of specimen testing showed that the smallest roughness value occurred of a spindle speed of 1200 rpm with a value of Ra = 0.810 µm and the output of observations make use macro photos represent that the roughness structure of the material was very smooth compared to the production process on CNC machines using other spindle speeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".