Acoustic emission-based monitoring for size-controlled robotic powder grinding.
- 1Department of Applied Physics, The University of Osaka Osaka Japan [email protected] [email protected].
Abstract
Controlling particle size is essential in solid-state synthesis, yet powder grinding remains a critical and largely unresolved challenge in self-driving laboratories. Here, we present a robotic mortar-based grinding system integrated with structure-borne acoustic emission (AE) sensing for autonomous powder processing. By measuring high-frequency elastic waves transmitted through the mortar, the proposed approach enables robust AE acquisition in standard laboratory environments without specialized acoustic facilities and provides a practical basis for particle-size-dependent feedback and autonomous stopping. We model AE-derived total spectral power as a monotonic function of particle size using monotone Bernstein polynomial regression, enabling size-dependent threshold design for grinding termination. Using this threshold-based stopping strategy, autonomous grinding experiments on NaCl and citric acid demonstrate reliable size-controlled processing. In contrast, monosodium glutamate exhibits degraded threshold reliability at intermediate target sizes, where AE fluctuations approach the noise floor. These results reveal material-dependent regimes in which AE-based feedback is effective only when sufficient signal-to-noise ratio is maintained, thereby clarifying the intrinsic limitations of AE-driven autonomous powder processing in solid-state self-driving laboratories.
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BibTeX
@article{nakajima2026acoustic,
title = {{Acoustic emission-based monitoring for size-controlled robotic powder grinding.}},
author = {Yusaku Nakajima and Ryusei Takamoto and Yasuo Takeichi and Kanta Ono},
journal = {RSC advances},
year = {2026},
month = oct,
doi = {10.1039/d6ra06905c},
url = {https://europepmc.org/article/MED/42921022},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Acoustic emission-based monitoring for size-controlled robotic powder grinding.
AU - Yusaku Nakajima
AU - Ryusei Takamoto
AU - Yasuo Takeichi
AU - Kanta Ono
PY - 2026
DA - 2026/10/23
JO - RSC advances
DO - 10.1039/d6ra06905c
UR - https://europepmc.org/article/MED/42921022
AB - Controlling particle size is essential in solid-state synthesis, yet powder grinding remains a critical and largely unresolved challenge in self-driving laboratories. Here, we present a robotic mortar-based grinding system integrated with structure-borne acoustic emission (AE) sensing for autonomous powder processing. By measuring high-frequency elastic waves transmitted through the mortar, the proposed approach enables robust AE acquisition in standard laboratory environments without specialized acoustic facilities and provides a practical basis for particle-size-dependent feedback and autonomous stopping. We model AE-derived total spectral power as a monotonic function of particle size using monotone Bernstein polynomial regression, enabling size-dependent threshold design for grinding termination. Using this threshold-based stopping strategy, autonomous grinding experiments on NaCl and citric acid demonstrate reliable size-controlled processing. In contrast, monosodium glutamate exhibits degraded threshold reliability at intermediate target sizes, where AE fluctuations approach the noise floor. These results reveal material-dependent regimes in which AE-based feedback is effective only when sufficient signal-to-noise ratio is maintained, thereby clarifying the intrinsic limitations of AE-driven autonomous powder processing in solid-state self-driving laboratories.
SN - 2046-2069
ER -