Title :
Pattern recognition of fatigue damage acoustic emission signal
Author :
Qian, Wenxue ; Xie, Liyang ; Huang, Dayan ; Yin, Xiaowei
Author_Institution :
Sch. of Mech. Eng. & Autom., Northeastern Univ., Shenyang, China
Abstract :
Acoustic emission (AE) of material is a common phenomenon; In fact, it has relation with the material states. In this paper, the acoustic emission test method is used and the characteristics of different phases of aluminum alloy cracks are analyzed. Artificial neural network (ANN) is used to recognize the pattern of AE of fatigue cracks. Practical test shows the above method could test the cracks that common method could not test. The result of pattern recognition is rather accurate, and is of practical engineering significance.
Keywords :
acoustic emission testing; aluminium alloys; condition monitoring; fatigue cracks; mechanical engineering computing; neural nets; pattern recognition; ANN; acoustic emission signal; acoustic emission test method; aluminum alloy; artificial neural network; fatigue cracks; fatigue damage; pattern recognition; Acoustic emission; Acoustic materials; Acoustic signal detection; Acoustic testing; Acoustical engineering; Condition monitoring; Fatigue; Internal stresses; Pattern recognition; Phase change materials; Acoustic Emission; fatigue crack; pattern recognition;
Conference_Titel :
Mechatronics and Automation, 2009. ICMA 2009. International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4244-2692-8
Electronic_ISBN :
978-1-4244-2693-5
DOI :
10.1109/ICMA.2009.5246614
