Impact damage characterisation in composite laminates

The overall purpose of this research is to detect and quantify low-velocity impact damage in structures made from composite materials. This research represents a study using simplified coupon specimens. The composite material chosen for the current research is a woven Carbon Fibre Reinforced Polymer (CFRP) prepreg with a MTM57 resin system (42%RW) with CF2900 fabric (280 g/m2, 12K and 2 x 2 twill fabric). This woven material was fabricated to produce coupon size specimens of 250 mm x 150 mm with II, 12 and 13 layers of thickness. Piezoelectric sensors of type SONOX® P5 were placed on three different locations on each of the coupon size specimens to record the responses along different directions of the ply and at different distances from the impact events. Two different approaches were used to record the acceleration response signals resulting from the impact excitation. The first approach used the LMS Testlab Impact Modal Analysis environment in order to acquire time data and produce spectra for a number of non-damaging impacts from a standard instrumented impulse hammer. The second approach used an instrumented drop-test rig to perform the potentially damaging impacts. The impact energies for this approach were set to range from 0.37 J to 41.72 J. The response signals from each test specimen were recorded using the LMS SCADAS III data acquisition system and saved for evaluation. To gather the appropriate information to make inferences regarding the extent of the damage, two different methods were used to estimate the damaged area. The first method measured the damage size using a vernier caliper directly on the impacted surface. The second method used developed X-ray films. For the latter method, the damage area was estimated as the rectangular area bounded by the width and length of the largest flaws visible parallel to the two plate axes. The correlation between the damage area in terms of the impact energy and force detected is presented and discussed. In this research, following a systematic series of experiments on the induction of impact damage in composite specimens, Scanning Electron Microscopy was used to inspect the topographies of the impacted surface at high magnifications. Two different approaches were used here to observe the type of failure modes. The first observation was on the surface defects of the impacted samples whilst the second type, usually categorised as destructive testing, visualised the cross-sectional defects to look at the internal damages. A damage model and damage pattern was developed from this work, which can provide sufficient information on the type and extent of damage. Both the damage model and pattern can be used to provide fundamental understanding of damage and failure mode progression in carbon fibre reinforced compo~ites with varying layer numbers and impact energies. Wavelet analysis is a well-known and powerful approach to feature extraction for problems in condition monitoring and damage detection. In this research, it is applied in the context of impact damage detection and quantification. The approach was based on response time signals recorded from the piezoelectric sensors. Damage indices in terms of Root Mean Square, Power Spectrum Density and Envelope Mean were presented. The results show that all three potential damage indices show a monotonic increase with impact energy and this behaviour is important when damage needs to be detected directly from the impact data. The current research was based on the idea of implementing machine-learning methods to identify and categorise (damaging and non-damaging) impacts using structural response data. To implement this idea, a novelty detection method using outlier analysis was used. This method has proved to be a successful in separating the damaged and non-damage features and classifying the types of failure modes. This method was considered an excellent approach to identify and categorise the impact events using structural response data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo