Optimization of Cutting Conditions in Vibration Assisted Drilling of Composites via a Multi-Objective EGO Implementation
Why this work is in the frame
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Bibliographic record
Abstract
A recent and promising technique to overcome the challenges of conventional drilling is vibration-assisted drilling (VAD) whereby a controlled harmonic motion is superimposed over the principal drilling feed motion in order to create an intermittent cutting state. Two additional variables other than the feed and the speed are introduced, namely the frequency and the amplitude of the imposed vibrations. Optimum selection of cutting conditions in VAD operations of composite materials is a challenging task due to several reasons; such as the increase in the number of controllable variables, the need for costly experimentation, and the limitation on the number of experiments that can be performed before tool degradation becomes an issue in the reliability of measurements. Additionally, there are often several objectives to consider, some of which may be conflicting, while others may be somewhat correlated. Pareto-optimality analysis is needed for conflicting objectives; however the existence of several objectives (high-dimension Pareto space) makes the generation and interpretation of Pareto solutions difficult. An attractive approach to the optimization task is thus to employ Kriging meta-models in a multi-objective efficient global optimization (m-EGO) framework for incremental experimentation of optimal setting of the cutting parameters. Additional challenge posed by constraints on machine capabilities is accounted for through domain transformation of the design variables prior to the construction of the Kriging models. Study results using a baseline exhaustive experimental data shows opportunity for employing m-EGO for the generation of well distributed Pareto-frontiers with fewer experiments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it