Acoustic properties of natural fiber reinforced composite micro-perforated panel designed using 3D printing
Bibliographic record
Abstract
The present study investigated the acoustic performance of biodegradable MPP absorbers made of natural fiber-reinforced composites (NFRC) using 3D printing. The novelty of this current research lies in the recent development of a methodology that aids industry professionals in optimizing the production of MPP (Micro Perforated Panel) at a competitive cost. This is achieved by addressing and eliminating issues commonly faced in traditional manufacturing processes, such as manual preparation and pressing. The FDM technique was used to fabricate test samples utilizing the PLA/corkwood composite. Using an impedance tube device with two microphones, the acoustic absorption coefficients of MPPs with different perforation diameters, thicknesses, and perforation rates were measured. Maa's analytical model was used to predict the acoustic absorption performance. Moreover, considering the average sound absorption and total cost of fabricating the samples, RSM-CCD was employed to optimize these samples. In the end, the parallel arrangement of MPP double layer and the combination of MPP with kenaf porous material were tested to improve the sound absorption performance. The results showed that the average sound absorption coefficient of the NFRC-MPP sound absorber is 25% more than that of conventional MPP sound absorbers.
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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".