Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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A Study on Acoustic Signal Characterization for Al and Steel Machining by Audio Deep Learning

오디오 딥러닝을 활용한 Al, Steel 소재의 절삭 깊이에 따른 오디오 판별

  • Kim, Tae-won (Graduate School of Mechanical Engineering, Keimyung Univ.) ;
  • Lee, Young Min (Dept. of Robotics and Mechanical Engineering, Korea Polytechnics Univ.) ;
  • Choi, Hae-Woon (Dept. of Mechanical Engineering, Keimyung Univ.)
  • 김태원 (계명대학교 대학원 기계공학과) ;
  • 이영민 (한국폴리텍대학 로봇캠퍼스 로봇기계과) ;
  • 최해운 (계명대학교 기계공학전공)
  • Received : 2021.06.09
  • Accepted : 2021.07.01
  • Published : 2021.07.31
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Abstract

This study reports on the experiment of using deep learning algorithms to determine the machining process of aluminium and steel. A face cutting milling tool was used for machining and the cutting speed was set between 3 and 4 mm/s. Both materials were machined with a depth to 0.5mm and 1.0mm. To demonstrate the developed deep learning algorithm, simulation experiments were performed using the VGGish algorithm in MATLAB toobox. Downcutting was used to cut aluminum and steel as a machining process for high quality and precise learning. As a result of learning algorithms using audio data, 61%-99% accuracy was obtained in four categories: Al 0.5mm, Al 1.0mm, Steel 0.5mm and Steel 1.0mm. Audio discrimination using deep learning is derived as a probabilistic result.

Keywords

Acknowledgement

This research is supported by NRF (2019R1F1A1062594).

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