{"title":"Ultrasonic measurement and detection of precursor delamination damage in composite under tension-torsion loading","authors":"S. Patra, Sourav Banerjee","doi":"10.1109/CMI.2016.7413796","DOIUrl":null,"url":null,"abstract":"Precursor to Damage state quantification in composite material is extremely challenging in the field of structural health monitoring (SHM). Conventional ultrasonic technique is not able to predict the early damage state; this could lead to catastrophic failure of the structure. So, early state damage detection is very imperative for safety and operation of structure. Composite materials experience different type of loading condition (e.g., Tension, torsion bending, etc.) during its operation in extreme environment. Precursor to damage in the composite material can appear in the form matrix cracking, fiber breakage and delamination. In this work, we presented an on board damage detection technique for precursor damage state quantification of Carbon fiber composite material (CFRP). An American society of testing and materials (ASTM) standard specimen was tested under tensor-torsion fatigue lading. Pitch-catch experiments were performed at a regular interval of 10,000 cycles and ultrasonic imaging were performed by using scanning acoustic microscope (SAM) to examine the onset of damage on surface as well as inside the material. Optical microscopy was also performed to examine the damage onset on the surface of the material. Advance signal processing techniques such as Discrete Fourier Transform (DFT), Short-time Fourier transform (STFT) and Continuous Wavelet Transform (CWT) were performed to analyze the sensor signal for extract information of damage growth with fatigue loading to prove that the precursor damage quantification is possible in online SHM.","PeriodicalId":244262,"journal":{"name":"2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMI.2016.7413796","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Precursor to Damage state quantification in composite material is extremely challenging in the field of structural health monitoring (SHM). Conventional ultrasonic technique is not able to predict the early damage state; this could lead to catastrophic failure of the structure. So, early state damage detection is very imperative for safety and operation of structure. Composite materials experience different type of loading condition (e.g., Tension, torsion bending, etc.) during its operation in extreme environment. Precursor to damage in the composite material can appear in the form matrix cracking, fiber breakage and delamination. In this work, we presented an on board damage detection technique for precursor damage state quantification of Carbon fiber composite material (CFRP). An American society of testing and materials (ASTM) standard specimen was tested under tensor-torsion fatigue lading. Pitch-catch experiments were performed at a regular interval of 10,000 cycles and ultrasonic imaging were performed by using scanning acoustic microscope (SAM) to examine the onset of damage on surface as well as inside the material. Optical microscopy was also performed to examine the damage onset on the surface of the material. Advance signal processing techniques such as Discrete Fourier Transform (DFT), Short-time Fourier transform (STFT) and Continuous Wavelet Transform (CWT) were performed to analyze the sensor signal for extract information of damage growth with fatigue loading to prove that the precursor damage quantification is possible in online SHM.