Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling

Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling

This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and...

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Bibliographic Details
Main Authors: Andrés Sio-Sever, Juan Manuel Lopez, César Asensio-Rivera, Antonio Vizan-Idoipe, Guillermo de Arcas
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Sensors
Subjects:
acoustic emission
machining
milling
process monitoring
microphone
data-driven modelling
Online Access:https://www.mdpi.com/1424-8220/22/10/3807

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