Comparative Analysis of Aluminium 6061-T6 With Different Stock Leaves Setting on Holes True Position: A Case Study
Bibliographic record
Abstract
With the evolution of industrial technology, improved machining processes have been developed. With the expansion of machining processes, machining characteristics alter according to the tool and work materials, tools used, machining technique used, etc., increasing surface smoothness, dimensional accuracy, and tool wear. Geometric error is a problem in precision machining due to parameter settings. This research aims to analyse the effect of stock leave setup on achieving precise product hole positions and improving machining process efficiency. The substrate involved was AL6061-T6. This study involved the use of MasterCAM software for simulation analysis to predict the effectiveness of the machining process. Then, the machining process was implemented with different stock leaves of 4 mm and 1 mm using a 5-axis Mazak machine. The result reveals the coordinate measurement machine (CMM) reading is proportional to the stock leave setup. The small reading value of CMM produces better accuracy cutting in machining process. In addition, a small value of stock leave produces a hole position within the specification due to adding additional processes compared to the high value of stock leave. Thus, with suitable parameters, all machining processes can produce a good accuracy of finishing parts and meet customer requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".