부산대학교 도서관

The acoustic emission monitoring with artificial neural networks predicts the ultimate strength of glass/epoxy composite laminates using Acoustic Emission Data. The ultimate loads of all the specimens were used to characterise the emission of hits during failure modes. The six layered glass fiber laminates were prepared (in woven mat form) with epoxy as the binding medium by hand lay-up technique. At room temperature, with a pressure of 30 kg/cm2, the laminates were cured. The laminates of standard dimensions as per ASTM D3039 for the tensile test were cut from the lamina. The Acoustic Emission (AE) test was conducted on these specimens under the load of uni-axial tension in the 10 Ton capacity Universal Testing Machine (UTM). In the monitoring process, acoustic emission parameters such as hits, counts, energy, duration, Root Mean Square (RMS) Value and amplitude were recorded. The RMS Values corresponding to the amplitude ranges from tensile test were used to characterise the failure load of all the similar glass-epoxy composite specimens. [ABSTRACT FROM AUTHOR]