Impact Damage Modelling of Composite Laminates Using Statistical Methods to Assess Strength Reduction for Rapid Design

Student thesis: Doctoral ThesisPhD

Abstract

Owing to a combination of the difficulty in detection and disproportionately large componentstrength reduction, barely visible impact damage (BVID) induced by low energyimpacts is a major concern to composite component manufacturers in the aerospace industry.Component testing to address this issue is both costly and time consuming. Thisresults in manufacturers oversizing components, which negates the weight saving benefitsthat composite components offer over traditional metal equivalents. Therefore, the aim ofthis study is to present simple statistical damage models built from empirical observationsthat can aid the component design process by replacing initial impact testing. To assessthe applicability of the impact damage models in the design process they are applied asthe input to a semi-numerical compression after impact strength reduction model, theresults of which are then compared to experimentally obtained data.To produce an empirical impact model a large database of impact damage is required,unlike any seen in contemporary literature. To this end, an extensive composite impactdamage database has been constructed from impact testing of over 40 laminates, resultingin over 1400 unique damage observations. Variations in material, ply count and layup areall considered to produce this diverse database.Data-driven generalised additive models (GAMs) are selected to model the database due totheir predictive capabilities when compared to both least-squares regression and machinelearning techniques. The resulting smooth models are shown to be accurate approximationsof the database. When applied to published damage data they are capable ofaccurate predictions, even when required to extrapolate beyond the limits of the variablesin the damage database. The smooth models are approximated by simple polynomialregression fits in an attempt to create a reduced predictive model that is less accuratebut more simple. These simpler models produce adequate approximations of the mean ofthe database, however model the variance poorly. They are also shown to be incapable ofpredicting the published data, becoming unstable when required to extrapolate outside ofthe database.Improvements to the semi-analytical model first presented by Choudhry, Rhead, Nielsen& Butler in their 2019 paper ’A plate model for compressive strength prediction of delaminatedcomposites’ [1] are presented. 17 layups, consisting of 16 and 24 ply HTS/977-2 andT800/M21 laminates, have been subjected to 4 impact energies and crushed. Assessment of the model updates on this dataset show that the model is improved, producing consistently more accurate predictions across the layups and thicknesses tested. The updated compression after impact (CAI) strength model is applied to the experimentally obtained CAI strength data using the measured impact damage, smooth models and simple polynomial models as damage inputs. The results of this show that the smooth damage model is an acceptable alternative to experimentally obtained damage data when used as the compression model input. Accurate strength predictions are shown for both the in-house experimental testing as well the published data for BVID impacts. The combination of the smooth damage model and semi-analytical CAI strength model is shown to produce quick and accurate component strength reduction predictions across a wide range of layups, impact energies and materials.
Date of Award14 Sep 2022
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorAndrew Rhead (Supervisor) & Karim Anaya-Izquierdo (Supervisor)

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