A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors. (July 2022)

• Record Type: Journal Article
• Authors: Li, Zhixiong; Liu, Xihao; Incecik, Atilla; Gupta, Munish Kumar; Królczyk, Grzegorz M.; Gardoni, Paolo
• Is Part Of: Journal of manufacturing processes. Volume 79(2022)
• Page Start: 233
• Page End: 249
• Publication Date: 2022-07
• Subjects: Machining -- Tool wear -- Audio signal processing -- Intelligent detection -- Sensors; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2022.04.066
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: Tool wear is an important parameter in the machining because the production, cost and performance is highly depend upon its performance. Therefore, the monitoring of cutting tool wear plays an important role in mechanical machining processes. With this aim, the present work deals with the application of novel ensemble deep learning model for cutting tool wear monitoring using audio sensors. The tool wear data during machining was extracted with an audio denoising technique combined with Fast Fourier Transform (FFT) and bandpass filters and dependent component analysis (DCA). Then, the ensemble convolutional neural networks (CNN) detection model was trained and audio signals were converted into audio images with different algorithms. Finally, the results confirm that this novel method is very accurate to predict the tool wear values under different cutting conditions. Highlights: An audio-based tool wear monitoring method is proposed. A new denoising model is developed for the audio signals. A ensemble deep learning model is developed for tool wear degree identification.