A Design of Experiment to Study the Effects of Cutting Speed and Feed on the Generated Drilling Thrust and Torque in Aluminum Alloys
Bibliographic record
Abstract
This work reports on an empirical study to account for the effects of the drilling parameters of cutting speed: V, m/s (max at the outer periphery), tool feed (f, mm/rev)), and drill diameter (D, mm) on the generated thrust (T, N) and torque (M, N.m). A total of 50 drilling tests were conducted on a vertical machining center based on design of experiment (DOE) test matrix using JMP-SAS/STAT® software. The test matrix employed 2 different aluminum alloys: one wrought (6061-T6) and another cast (A356-T6) with both tempered to the T6 condition. Two classical chisel drills of different diameters (10mm and 12.7mm) were used. Drilling parameter variables employed varied from very mild to very aggressive combinations including 5 values of spindle speeds (796, 1592, 3183, 6366, and 9868 rpm) and 5 values of drilling feeds (0.04, 0.08, 0.16, 0.32, 0.64 mm/rev). Drilling forces and torques were recorded using a 4-component dynamometer (Kistler model 9123) at sampling rate of 200 Hz. At full drill engagement, cutting torque and thrust status were identified for each drilling test case. By fitting to the measured drilling forces, power law equations with parametric variables D, f, and V are developed. Initial values of the model’s power coefficients were initially identified using Matlab® using a least square optimization function with target to minimize the difference between the predicting model and the experimental data. These values were fed as initially-guessed values to the nonlinear modeling Gauss method in JMP-SAS/STAT® with 50 observations for each set of experiments. For both torque and thrust and for both aluminum alloys employed, the model’s coefficient values were identified setting the convergence criteria to 10−15 with total of 60 iterations. For both thrust and torque, it was found that the power coefficients for feed and cutting speed are statistically significant (better than p-values < 0.05) with values of about 0.7 and −0.1, respectively.
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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 | 0.000 | 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".