On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

Prasetyo, Teguh Budi; Amar, Said; Arendra, Anis; Zami, M K Zam

doi:10.1088/1742-6596/953/1/012144

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Li et al [ 56 ] used three condenser microphones (ECOOPRO EO-200) to collect real-time audio signals from different directions and distances. Prasetyo et al [ 57 ] placed an omni-directional Andoer microphone with a frequency response between 20 Hz to 16 kHz on the tool post to capture audio signal, recorded it on MATLAB at a sampling rate of 44.1 kHz and a sampling size of 1024, and then processed the signal. The signal feature was extracted within the frequency domain by using a fast Fourier transform (FFT), and the feature signal was inputted into an artificial neural network (ANN) for training to distinguish between the signals from the normal tool and the worn tool.…”

Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%

“…Noise signal exhibits the characteristics of unpredictability and high destructiveness, and it exerts a considerable negative effect on the processing of the target signal. After capturing audio signals and recording them in Matlab, Prasetyo et al [ 57 ] used Fast Fourier transform to extract signal features in the frequency domain. Then, artificial neural networks were used to classify tool wear.…”

Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%

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Research Progress of Noise in High-Speed Cutting Machining

Wei

Shang

Peng

et al. 2022

Sensors

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High-speed cutting technology has become a development trend in the material processing industry. However, high-intensity noise generated during high-speed cutting exerts a potential effect on the processing efficiency, processing accuracy, and product quality of the workpiece; it may even cause hidden safety hazards. To conduct an in-depth study of noise in high-speed cutting machining, this work reviews noise sources, noise collection and numerical recognition, noise control, and condition monitoring based on acoustic signals. First, this article introduces noise sources, noise signal acquisition equipment, and analysis software. It is pointed out that how to accurately classify and recognize the target signal in the complex high-speed machining environment is one of the focuses of scholars’ research. Then, it points out that a computer achieves high accuracy and practicability in signal analysis, processing, and result display. Second, in the aspect of noise signal processing, the characteristics of noise signals are analyzed. It is pointed out that accurately analyzing the characteristics of different noise source signals and adopting appropriate methods for identification and processing are the necessary conditions for effectively controlling and reducing the noise in the process of high-speed cutting. The advantages and applicable fields of artificial intelligence algorithms in processing mixed noise source signals with different frequency characteristics are compared, providing ideas for studying the mechanism of noise generation and the identification of noise sources. Third, in terms of noise control, a detailed overview is provided from the aspects of the treatment of the noise source that contributes the most to the overall noise, the improvement of the tool structure, the optimization of cutting parameters, and the analysis of contact factors between the tool and the workpiece. It provides an effective way for noise control in the process of high-speed cutting. In addition, the application of acoustic signals to condition monitoring is also thoroughly analyzed. The practical application value of condition monitoring based on acoustic signals in high-speed machining is highlighted. Finally, this paper summarizes the positive significance of noise research in high-speed machining and identifies key problems and possible research methods that require further study in the future.

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Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%

Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%