Identification of Fiber-reinforced Plastic Failure Mechanisms from Acoustic Emission Data using Neural Networks

2005 ◽  
Vol 40 (3) ◽  
pp. 193-226 ◽  
Author(s):  
Nat Ativitavas ◽  
Thanyawat Pothisiri ◽  
Timothy J. Fowler
Author(s):  
Daoxiang Wei ◽  
Yuqing Yang ◽  
Jun Si ◽  
Xiang Wen

Abstract Fiber reinforced plastics are used in pressure vessel manufacturing because of their high strength and corrosion resistance.Defects may occur in the manufacture and use of the pressure vessel. To ensure safe operation of the pressure vessel, it is necessary to conduct periodic safety assessment of the pressure vessel put into operation. It is difficult to evaluate the safety status of fiber-reinforced plastic pressure vessels by conventional nondestructive testing.Acoustic emission detection technology is a dynamic detection method, which has obvious advantages for the performance and fracture process of fiber reinforced plastic materials. ASME section V or ASTM section on acoustic emission detection of FRP pressure vessels, in which the localization of defects is mainly based on acoustic emission instruments. Due to the anisotropy of FRP material, the instrument can only give the area of the defect, and then use other non-destructive testing methods supplementary detection, so the author proposes a regional positioning method, which can locate defects more accurately. In this paper, acoustic emission detection method and lead breaking method were used to simulate the deficiency, and acoustic velocity attenuation and variation of fiber reinforced plastics were studied, and confirmative tests were carried out to obtain the positioning accuracy of the deficiency in different areas.In order to achieve the acoustic emission (AE) response behavior of stretching damage of glass fiber composites with fiber pre-broken and weak bonding, stretching tests and real-time AE monitoring of glass fiber composites were conducted.Experimental results showed that damage model such as matrix cracking and fiber fracture and bending could be occurred in the process of damage and failure. The composition and content of signal frequency of AE is also different because of difference of preset defect.


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