Active Vibration Control with State Feedback in Woodcutting
Bibliographic record
Abstract
Circular saws are widely used in wood processing for applications ranging from primary lumber manufacturing to furniture industry and home workshops. They directly contribute to production problems such as poor cutting accuracy, poor surface quality, short tool life, high noise levels, and excessive raw material wastage. Vibration of the saw blade during woodcutting has been identified as a key reason for poor wood recovery. In fact, about 12% of the raw material in woodcutting ends up as sawdust due to excessive sawing gap. Efficient wood sawing is being pursued with the objective of mitigating these problems; particularly, to reduce saw blade vibration and sawdust. In this paper, we present active control of saw blade vibration using linear quadratic Gaussian (LQG) control. We outline a test rig that has been developed for our experimental investigation. The system configuration is described and the control problem is formulated. The experimental procedure for identification of a system model is described. The implementation of the LQG control scheme is outlined, and typical results from the experimental control system are presented and discussed. The developed controller is shown to be very effective in the present application, as evident from the results that have been obtained. In particular, the amplitude of the saw blade vibration has been reduced by 66% on average using active control, compared to cases with no control. Also, the cutting gap (kerf) has been reduced by 25%, from 2.00 mm to 1.50 mm, through active control. In terms of 1995 prices, this would correspond to an increased revenue of $640,000 per year for a mill producing 100 MM fbm of lumber annually.
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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 | 0.000 |
| 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) | 0.001 | 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".