• 한국소음진동공학회논문집
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• 제17권6호
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• pp.507-514
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• 2007

This paper reports on the research which investigates acoustic signals acquired in turning with rough and finish simultaneously. The material is aluminum thin pipe. Two acousto-ultrasonic sensors were set on the finish and the rough bite of the CNC machine. It was first evaluated that one source was affected by the other. It was found that two signals were little affected each other, and that the acoustic signal from the finish bite was more related to the surface defects. Signals from the finish bite only were then analyzed in order to observe several types of surface defects. Second the fundamental experiments were accomplished to study the effects of machine vibration and material state. The signal characteristics due to surface defects were studied from the collected acoustic signals. The analysis was based on real time data, root mean squared average and frequency spectrum by fast fourier transform. As a result, the acoustic signals were made effects by machine condition, material structure. The acoustic signal from the finish bite was closely correlated with surface quality. Two types surface micro defects were then evaluated by the signal characteristics.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.457-462
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• 2007

This paper reports on the research which investigates acoustic signals acquired in turning with rough and finish simultaneously. The material is aluminum thin pipe. Two acoustic sensors were set on CNC machine. One was set on the finish bite and the other the rough. Two signals were first analyzed in order to consider how much the acoustic signal from the finish bite was coupled by that from the rough. A simple data collecting system to acquire signals from the finish was then determined because two acoustic signals were little coupled. Second the fundamental experiments were accomplished to study the effects of machine vibration and material state. The signal characteristics due to surface defects were studied from the collected acoustic signal data. The signal analysis was based on real time data, root mean squared average and frequency spectrum by fast fourier transform. As a result, the acoustic signals were made effects by machine condition, material structure. The acoustic signal from the finish bite was closely correlated with surface quality. Two types surface micro defects were then evaluated by the signal characteristics.