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The confluence of increasing global sustainability demands and the imperative for real-time quality control in advanced manufacturing systems provides the foundation for this study. We address this by initially conceptualizing an active robotic noise-mitigation panel based on flax fiber designed for autonomous deployment in landscaping and urban noise control environments. The feasibility of this active system, which relies on the dynamic manipulation of a passive flax core integrated with AI-driven sensing and actuation, is fundamentally dependent on precise and rapid assessment of the raw fiber’s acoustic potential and physicomechanical quality. Confronting the scarcity of suitable non-destructive pre-assessment techniques, this paper details the subsequent development and comprehensive validation of a novel methodology utilizing the sound absorption effect to characterize flax fiber quality. A specialized device was engineered and rigorously optimized, establishing critical operational parameters: a 10 g sample mass, an emitter frequency of 1750 Hz (optimally aligned with the λ/4 thickness), and a reference moisture content of 12%. The research successfully established a robust statistical correlation between the acoustic attenuation measurements and key industrial indicators, specifically linear density, breaking load, and flexibility. Statistical validation using the Student’s t-test confirmed a high degree of agreement with established standards (DSTU 4015-2001), demonstrating excellent reproducibility and high precision (relative expanded uncertainty below 5 %). Furthermore, empirical power and logarithmic regression formulas were derived to enable the direct calculation of quality parameters from the acoustic data. This integrated approach not only provides a reliable, rapid, and objective tool for industrial quality control, but also furnishes the essential material assessment capability required to transition sustainable flax materials into the demanding domain of smart, active noise-mitigation technologies.
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Tolmachov et al. (2026) studied this question.
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