Fractional Calculus-Based Energy Efficient Active Chatter Control of Milling Process Using Small Size Electromagnetic Actuators
Bibliographic record
Abstract
Abstract For a larger depth of cutting above a certain critical value, self-excited vibrations occur in case of milling operations. This phenomenon of unstable milling tool vibrations is called chatter and is the main cause of the workpiece surface finish deterioration. The working life of the milling tool decreases substantially if the chatter is ignored. Active chatter control technique using the fractional order control methodology is investigated in the present work. Controller parameters are optimized by using the pattern search optimization technique. Electromagnetic actuators are used to generate the required control forces. The proposed technique is compared with the optimal loop shaping (LS) robust controller and optimal traditional proportional-derivative controller. It has been observed that the chatter can be avoided with relatively much less amplitude of control forces using the proposed controller. This aspect not only reduces the size of the required actuators but substantially reduces the control energy required to maintain stability. With the proposed controller, there is 168% saving in the control energy compared with the widely used robust control strategy. The robustness properties of the proposed controller are comparable with the loop shaping robust controller. Experimental results verify the efficiency and robustness of the proposed method.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".