Tool Wear Prediction in CNC Turning Operations using Ultrasonic Microphone Arrays and CNNs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916285514776576 |
|---|---|
| author | Steckel, Jan Aerts, Arne Verreycken, Erik Laurijssen, Dennis Daems, Walter |
| author_facet | Steckel, Jan Aerts, Arne Verreycken, Erik Laurijssen, Dennis Daems, Walter |
| contents | This paper introduces a novel method for predicting tool wear in CNC turning operations, combining ultrasonic microphone arrays and convolutional neural networks (CNNs). High-frequency acoustic emissions between 0 kHz and 60 kHz are enhanced using beamforming techniques to improve the signal- to-noise ratio. The processed acoustic data is then analyzed by a CNN, which predicts the Remaining Useful Life (RUL) of cutting tools. Trained on data from 350 workpieces machined with a single carbide insert, the model can accurately predict the RUL of the carbide insert. Our results demonstrate the potential gained by integrating advanced ultrasonic sensors with deep learning for accurate predictive maintenance tasks in CNC machining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tool Wear Prediction in CNC Turning Operations using Ultrasonic Microphone Arrays and CNNs Steckel, Jan Aerts, Arne Verreycken, Erik Laurijssen, Dennis Daems, Walter Audio and Speech Processing Artificial Intelligence Sound Signal Processing This paper introduces a novel method for predicting tool wear in CNC turning operations, combining ultrasonic microphone arrays and convolutional neural networks (CNNs). High-frequency acoustic emissions between 0 kHz and 60 kHz are enhanced using beamforming techniques to improve the signal- to-noise ratio. The processed acoustic data is then analyzed by a CNN, which predicts the Remaining Useful Life (RUL) of cutting tools. Trained on data from 350 workpieces machined with a single carbide insert, the model can accurately predict the RUL of the carbide insert. Our results demonstrate the potential gained by integrating advanced ultrasonic sensors with deep learning for accurate predictive maintenance tasks in CNC machining. |
| title | Tool Wear Prediction in CNC Turning Operations using Ultrasonic Microphone Arrays and CNNs |
| topic | Audio and Speech Processing Artificial Intelligence Sound Signal Processing |
| url | https://arxiv.org/abs/2406.08957 |