Machinability of Heat-Treated 356 and 319 Aluminum Alloys: Methodology for Data Processing and Calculation of Drilling Force and Moment
Bibliographic record
Abstract
Abstract Heat treated Al-Si-Mg and Al-Si-Cu-Mg cast alloys, belonging to the Al-Si alloy system and represented respectively by 356 and 319 alloys containing mainly α-Fe-intermetallic and related to hardness levels of (100±10 HB), were selected for the machinability study, due to the high demand of these alloys in the automobile industry In this paper, one was provided with an introduction to the force and moment calculations that were used to evaluate the drilling processes as are outlined in a previous work.[1] A new technique was developed whereby a low pass filter was incorporated in the signal processing algorithm which was used in calculating the mean cutting force and moment during the drilling processes. All signals were independently monitored, digitized and recorded into Lab View. Universal Kistler DynoWare software was used for force measurements and data processing of cutting force and moments. Matlab programs were developed for data processing and for calculating the mean value of cutting force and moment and their standard deviations in drilling tests. The raw cutting force data were analysed using the application of a low pass filter and following the detection of points within each cycle in the signal in the drilling tests. 1600 sample points per cycle were acquired for calculating the mean value of cutting feed force (Fz) and 1200 sample points per cycle for the other five components of force and moment (Fx, Fy, Mx, My, and Mz) in each signal (115 cycle or hole/signal) however, only 200 sample points per cycle were used for standard deviation or peak-to-valley calculations. The low Mg-content 319 alloys (0.1%) yielded the longest tool life, more than two times that of 356 alloys (0.3%Mg) and one and half times longer than the high Mg-content 319 alloys (0.28%). It is customary to rate the machinability of the 319 alloy higher than 356 one and the machinability of the low Mg-content 319 alloy higher than the high Mg-content one.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".