Adaptive Control and Enhanced Algorithm for Efficient Drilling in Composite Materials
Bibliographic record
Abstract
Due to their inexpensive cost and superior qualities compared to conventional metals, Glass Fibre Reinforced Plastic (GFRP) composites are frequently used in engineering applications.Despite the development of numerous non-traditional drilling methods, traditional mechanical drilling methods based on CNC machines are still utilized as the primary applications for composites due to their financial advantages.Damage in the composite materials during the drilling process due to delamination often happens.The delamination has directly related to the drilling force.A dynamic model of the drilling force is a function of the feed rate.Due to the unpredictable nature of the composite material's physical and chemical properties, it may be challenging to realize the dynamics of the drilling process in this material.In this paper, the mathematical model of the drilling process is obtained experimentally based on system identification.Then, to address the problem of controlling the drilling force of composite materials, this paper proposes a Model Reference Adaptive Control (MRAC) based on the Enhanced Flower Pollination Algorithm (EFPA) to handle the uncertainties and time-varying dynamics of the drilling process.The performance of the proposed controller is evaluated based on the Integral Time of Absolute Error (ITAE) index.The simulation results show that the proposed controller is effective in avoiding drilling-induced delamination in composite under different operation conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".