Optimization of the Cutting Conditions for High Speed Drilling of Woven Composites
Bibliographic record
Abstract
The present work proposes a new algorithm for the optimization of cutting parameters in the high speed drilling of woven composites. The cutting parameters under consideration are the feed rate and the spindle speed. Three performance parameters are to be minimized. These are the exit delamination, the surface roughness and the thrust force. These performance parameters are observed experimentally. One of the challenges that face the experimental testing of these parameters is the high cost of the drilling tools and specimen materials. Therefore, the minimization of the number of experimental tests is a necessary requirement. The algorithm presented hybridizes Kriging as a meta-modeling technique with evolutionary multi-objective optimization to optimize the cutting parameters while intelligently selecting the new set of cutting parameters in each iteration. After starting with a factorial design of the search space, and after testing the performance criteria at these points, the algorithm fits a multi-dimensional surface using Kriging. This step is followed by an evolutionary search on the fitted model. The search spreads a population of search points in the direction of better performance criteria as well as in the direction of un-sampled space. The previous two steps are conducted iteratively for a pre-defined number of iterations. In the final iteration, the population of search points is clustered to yield a small number of new points at which the new experiments will be conducted. The whole process is iterated until the maximum number of allowable experiments is achieved. The algorithm is tested using an existing set of previously published experimental data that are dense enough to predict the actual response surface of the performance criteria. Results showed that the algorithm smartly moved into the direction of higher performance criteria with a low number of experimental trials.
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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".