MEMS Microphone-Driven Near-Sensor Reservoir Computing for Lightweight Tool Wear Classification in Milling
Journal
International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers
Journal Issue
2025
Start Page
1760
End Page
1763
ISSN
21670013
Date Issued
2025
Author(s)
Abstract
This paper presents a real-time tool wear monitoring method using a near-sensor reservoir computing system with a MEMS microphone. The system processes audio signals generated during the milling of S50C steel with a three-flute cutting tool through a nonlinear Mackey-Glass circuit that serves as the reservoir. The reservoir transforms raw audio signals into high-dimensional representations, and a linear regression classifier identifies the tool wear state. Compared to traditional frequency-domain methods using Fast Fourier Transform (FFT), this approach achieves higher accuracy, reduces computational complexity, and decreases latency. Experimental results validate the effectiveness of the method and practical potential in machining applications.
Event(s)
23rd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2025, Orlando, 29 June 2025 - 3 July 2025
Subjects
Mackey-Glass circuit
MEMS microphone
Milling
Reservoir computing
Tool wear prediction
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
conference paper