In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network

Prediction/detection of exit burrs is critical in manufacturing automation. In this research, an intelligent burr sensing/monitoring scheme is proposed. Acoustic emission (AE) was selected to detect burr formation during drilling. For effective extraction of information contained in the collected AE signals, wavelet transform (WT) was adopted and the selected features through WT were fed into a back-propagation artificial neural net (ANN) as input vectors. To validate the in-process AE monitoring system, both WT-based ANN and cutting condition-based ANN outputs (cutting speed, feed, drill diameter, etc.) were compared with experimental data. The results show that the proposed scheme is not only efficient with fewer inputs, but more reliable in predicting drilling burr types over cutting condition-based ANN.

[1]  David Dornfeld,et al.  Burr Formation in Drilling Miniature Holes , 1997 .

[2]  Sheng Liu,et al.  Programming deburring robots based on human demonstration with direct burr size measurement , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[3]  Philippe De Wilde.  Neural Network Models , 1996 .

[4]  David Dornfeld,et al.  Acoustic Emission Feedback for Precision Deburring , 1992 .

[5]  H. Suzuki,et al.  Wavelet transform of acoustic emission signals , 1996 .

[6]  Jinsoo Kim,et al.  Development of a drilling burr control chart for low alloy steel, AISI 4118 , 2001 .

[7]  G. Seliger,et al.  Sensor-Aided Programming and Movement Adaptation for Robot-Guided Deburring of Castings , 1991 .

[8]  A David Dornfeld.  In Process Recognition of Cutting States , 1994 .

[9]  Sang-Heon Lee,et al.  Optimisation of cutting parameters for burr minimization in face-milling operations , 2003 .

[10]  Matthew Zeidenberg.  Neural network models in artificial intelligence , 1990 .

[11]  Su-Hai Hsiang,et al.  Applying ANN to predict the forming load and mechanical property of magnesium alloy under hot extrusion , 2005 .

[12]  Gang Qi,et al.  Discrete wavelet decomposition of acoustic emission signals from carbon-fiber-reinforced composites , 1997 .

[13]  Hao Zeng,et al.  Application of artificial neural network in laser welding defect diagnosis , 2005 .

[14]  Gang Qi,et al.  ON PREDICTING THE FRACTURE BEHAVIOR OF CFR AND GFR COMPOSITES USING WAVELET-BASED AE TECHNIQUES , 1997 .

[15]  Homayoon Kazerooni,et al.  Automated Robotic Deburring of Parts Using Compliance Control , 1991 .

[16]  Yves Chauvin,et al.  Back-Propagation: Theory, Architecture, and Applications , 1995 .

[17]  Nagayoshi Kasashima,et al.  Online Failure Detection in Face Milling Using Discrete Wavelet Transform , 1995 .

[18]  L. K. Gillespie,et al.  Deburring precision miniature parts , 1979 .

[19]  Yoshimi Takeuchi,et al.  Automation of Deburring Operations by Means of Color Information , 1987 .

[20]  Seoung Hwan Lee.  BURR SIZE MEASUREMENT USING A CAPACITANCE SENSOR , 1999 .

[21]  Chih-Hsing Chu,et al.  Prediction and Simulation of Milling Burr Formation for Edge-Precision Process Planning , 2002 .