چکیده مقاله
This paper focuses on the use of principal component analysis PCA to classify different fracture signals from background noises PCA is a method used to simplify high order data sets to lower dimension for a simpler analysis Tensile tests carried out on glass fiber reinforced epoxy composites and acoustic emissions recorded from these tests The aim of this study is to classify the acoustic emission AE signal using PCA To reduce the multi linearity among AE parameters such as peak amplitude, frequency, duration time, count, etc and extract the significant AE parameters, correlation analysis utilized The experimental results show the successful separation of experimental fracture mode signals from the background noise
کلیدواژهها
نویسندگان
J. Taghizadeh
Non destructive Testing Laboratory, Department of Mechanical Engineering , Amir Kabir University of Technology, Tehran, Iran
M. Ahmadi Nadjafabadi
Non destructive Testing Laboratory, Department of Mechanical Engineering , Amir Kabir University of Technology, Tehran, Iran
شیوه ارجاع
Taghizadeh, J. and Ahmadi Nadjafabadi, M.,1390,Classification of acoustic emission signals collected during tensile tests on unidirectional glass/epoxy composites using principal component analysis,12th Iranian Conference on Manufacturing Engineering (ICME 2010),Tehran
ارائهشده در
مجموعه مقالات دوازدهمین کنفرانس ملی مهندسی ساخت و تولید ایران6 دی 1390 · تهران