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http://dx.doi.org/10.14775/ksmpe.2021.20.07.072

A Study on Acoustic Signal Characterization for Al and Steel Machining by Audio Deep Learning  

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.)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.20, no.7, 2021 , pp. 72-79 More about this Journal
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
Deep Learning; Machining; Audio Discrimination;
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