Abstract
In this study, an acoustic emission (AE) sensor was utilized to predict fractures that occur in a product during the sheet metal forming process. An AE activity was analyzed, presuming that AE occurs when plastic deformation and fracturing of metallic materials occur. For the analysis, a threshold voltage is set to distinguish the AE signal from the ripple voltage signal and noise. If the amplitude of the AE signal is small, it is difficult to distinguish the AE signal from the ripple voltage signal and the noise signal. Hence, there is a limitation in predicting fractures using the AE sensor. To overcome this limitation, the Kalman filter was used in this study to remove the ripple voltage signal and noise signal and then analyze the activity. However, it was difficult to filter out the ripple voltage signal using a conventional low-pass filter or Kalman filter because the ripple voltage signal is a high-frequency component governed by the switch-mode of the power supply. Therefore, a Kalman filter that has a low Kalman gain was designed to extract only the ripple voltage signal. Based on the KF-RV algorithm, the measured ripple voltage and noise signal were reduced by 97.3% on average. Subsequently, the AE signal was extracted appropriately using the difference between the measured value and the extracted ripple voltage signal. The activity of the extracted AE signal was analyzed using the ring-down count among various AE parameters to determine if there was a fracture in the test specimen.
Highlights
The occurrences of defects in sheet metal formation can cause huge losses in productivity
In this study, a Kalman filter was applied with a low Kalman gain to an algorithm that processes AE3 of 19 signals containing noise and ripple voltage signals to predict fractures in ductile materials during sheet metal forming
Kalman gain was applied to the signal processing algorithm to predict fractures in voltage signal from the power supply is a factor that further restricts the use of Acoustic emission (AE) sensors ductile materials during sheet metal forming
Summary
The occurrences of defects in sheet metal formation can cause huge losses in productivity. In this study, a Kalman filter was applied with a low Kalman gain to an algorithm that processes AE3 of 19 signals containing noise and ripple voltage signals to predict fractures in ductile materials during sheet metal forming. AE sensor, the ripple voltage process, it has not yet been applied to the metal sheet forming process for a ductile signal of the power supply is transmitted as the output through various paths.material. In a switchThe advantage of the proposed method is to detect the defect of the material, which has mode power supply, a switching regulator receives power from an alternating current a very high AE signal-to-noise ratio in real-time. Kalman gain was applied to the signal processing algorithm to predict fractures in voltage signal from the power supply is a factor that further restricts the use of AE sensors ductile materials during sheet metal forming. In the section: AE Detection Method and Discussion, it has been confirmed that the AE detection method can determine the accurate time at which a defect occurs in the product during the sheet metal forming process
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