Data fusion of distributed AE sensors for the detection of friction sources during press forming

The authors proposed a friction source detection system using multi-acoustic emission (AE) sensors and data fusion system in this study. In this work, three AE sensors are positioned at the metal forming tool for identification of the location of friction sources. The AE signals were processed by FFT and discrete wavelet transform (DWT), in order to reduce the noise and extract features of signals due to friction. The Fujimori method was employed to measure the arrival time from AE source to the distributed sensors. Furthermore, a database was constructed and employed to estimate the source location. The detection system was applied for measuring the friction during deep drawing of sheet metal. Several experiments were carried out by using the friction detection system and the results show that the location of friction source can be successfully detected by using the multi-AE sensors system even if location of friction resource on interface of the die and the workpiece is invisible.