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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3807 |
| _version_ | 1797495682364342272 |
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| author | Andrés Sio-Sever Juan Manuel Lopez César Asensio-Rivera Antonio Vizan-Idoipe Guillermo de Arcas |
| author_facet | Andrés Sio-Sever Juan Manuel Lopez César Asensio-Rivera Antonio Vizan-Idoipe Guillermo de Arcas |
| author_sort | Andrés Sio-Sever |
| collection | DOAJ |
| description | 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 a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm. |
| first_indexed | 2024-03-10T01:53:08Z |
| format | Article |
| id | doaj.art-99165230e96d46f48a7c8481e996fd99 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:53:08Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-99165230e96d46f48a7c8481e996fd992023-11-23T13:01:32ZengMDPI AGSensors1424-82202022-05-012210380710.3390/s22103807Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI ModellingAndrés Sio-Sever0Juan Manuel Lopez1César Asensio-Rivera2Antonio Vizan-Idoipe3Guillermo de Arcas4Grupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Ingeniería Mecánica, Universidad Politécnica de Madrid, 28040 Madrid, SpainGrupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Telemática y Electrónica, Universidad Politécnica de Madrid, 28040 Madrid, SpainGrupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Teoria de la Señal y Comunicaciones, Universidad Politécnica de Madrid, 28040 Madrid, SpainDepartamento de Ingeniería Mecánica, Universidad Politécnica de Madrid, 28006 Madrid, SpainGrupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Ingeniería Mecánica, Universidad Politécnica de Madrid, 28040 Madrid, SpainThis 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 a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.https://www.mdpi.com/1424-8220/22/10/3807acoustic emissionmachiningmillingprocess monitoringmicrophonedata-driven modelling |
| spellingShingle | Andrés Sio-Sever Juan Manuel Lopez César Asensio-Rivera Antonio Vizan-Idoipe Guillermo de Arcas Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling Sensors acoustic emission machining milling process monitoring microphone data-driven modelling |
| title | Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling |
| title_full | Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling |
| title_fullStr | Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling |
| title_full_unstemmed | Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling |
| title_short | Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling |
| title_sort | improved estimation of end milling parameters from acoustic emission signals using a microphone array assisted by ai modelling |
| topic | acoustic emission machining milling process monitoring microphone data-driven modelling |
| url | https://www.mdpi.com/1424-8220/22/10/3807 |
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